Paper Digest: WWW 2022 Highlights
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The Web Conference (WWW) is one of the top internet conferences in the world. In 2022, it is to be held online.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: WWW 2022 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Search Engines: From The Lab to The Engine Room, and Back: Keynote Talk Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Prabhakar Raghavan has given a Keynote Talk at The ACM Web Conference 2022 on Wednesday 27th April 2022. |
Prabhakar Raghavan; |
| 2 | How The Web Will Shape The Hybrid Work Era: Keynote Talk Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Jaime Teevan has given a Keynote Talk at The Web Conference on Friday 29th April 2022. |
Jaime Teevan; |
| 3 | Responsible AI: From Principles To Action: Keynote Talk Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Virginia Dignum has given a Keynote Talk at The ACM Web Conference 2022 on Thursday 28th April 2022. |
Virginia Dignum; |
| 4 | Regulatory Instruments for Fair Personalized Pricing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose two sound policy instruments, i.e., capping the range of the personalized prices or their ratios. |
Renzhe Xu; Xingxuan Zhang; Peng Cui; Bo Li; Zheyan Shen; Jiazheng Xu; |
| 5 | Allocating Stimulus Checks in Times of Crisis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the problem of financial assistance (bailouts, stimulus payments, or subsidy allocations) in a network where individuals experience income shocks. |
Marios Papachristou; Jon Kleinberg; |
| 6 | Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. |
Xiaoxiao Xu; Chen Yang; Qian Yu; Zhiwei Fang; Jiaxing Wang; Chaosheng Fan; Yang He; Changping Peng; Zhangang Lin; Jingping Shao; |
| 7 | BONUS! Maximizing Surprise Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In a two-player competition with n rounds, we aim to derive the optimal bonus size to maximize the audience’s overall expected surprise (as defined in [7]). |
Zhihuan Huang; Yuqing Kong; Tracy Xiao Liu; Grant Schoenebeck; Shengwei Xu; |
| 8 | Calibrated Click-Through Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We analyze the optimal information design in a click-through auction with stochastic click-through rates and known valuations per click. |
Dirk Bergemann; Paul Dütting; Renato Paes Leme; Song Zuo; |
| 9 | Price Manipulability in First-Price Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we adopt a metric that was introduced in the context of bitcoin fee design markets: the percentage change in payment that can be achieved by being strategic. |
Johannes Brustle; Paul Dütting; Balasubramanian Sivan; |
| 10 | Equilibria in Auctions with Ad Types Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper studies equilibrium quality of semi-separable position auctions (known as the Ad Types setting [9]) with greedy or optimal allocation combined with generalized second-price (GSP) or Vickrey-Clarke-Groves (VCG) pricing. |
Hadi Elzayn; Riccardo Colini Baldeschi; Brian Lan; Okke Schrijvers; |
| 11 | Optimal Collaterals in Multi-Enterprise Investment Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our main objective is to examine the role of collateral contracts in handling the strategic risk that can propagate to a systemic risk throughout the network in a cascade of defaults. |
Moshe Babaioff; Yoav Kolumbus; Eyal Winter; |
| 12 | On Designing A Two-stage Auction for Online Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we explicitly investigate the relation between the coarse and refined ad quality metrics, and design a two-stage ad auction by taking the decision interaction between the two stages into account. |
Yiqing Wang; Xiangyu Liu; Zhenzhe Zheng; Zhilin Zhang; Miao Xu; Chuan Yu; Fan Wu; |
| 13 | Auctions Between Regret-Minimizing Agents Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We analyze a scenario in which software agents implemented as regret-minimizing algorithms engage in a repeated auction on behalf of their users. |
Yoav Kolumbus; Noam Nisan; |
| 14 | Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Multi-granularity Residual Learning Framework (MRLF) for more effective time series prediction. |
Min Hou; Chang Xu; Zhi Li; Yang Liu; Weiqing Liu; Enhong Chen; Jiang Bian; |
| 15 | Almost (Weighted) Proportional Allocations for Indivisible Chores✱✱ Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study how to fairly allocate a set of indivisible chores to a number of (asymmetric) agents with additive cost functions. |
Bo Li; Yingkai Li; Xiaowei Wu; |
| 16 | Beyond Customer Lifetime Valuation: Measuring The Value of Acquisition and Retention for Subscription Services Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While customer lifetime value (LTV) is commonly used to do so, we demonstrate that LTV likely over-states the true value of acquisition or retention. |
Hamidreza Badri; Allen Tran; |
| 17 | Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work focuses on repeated first price auctions where bidders with fixed values for the item learn to bid using mean-based algorithms – a large class of online learning algorithms that include popular no-regret algorithms such as Multiplicative Weights Update and Follow the Perturbed Leader. |
Xiaotie Deng; Xinyan Hu; Tao Lin; Weiqiang Zheng; |
| 18 | Truthful Online Scheduling of Cloud Workloads Under Uncertainty Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a framework to reduce scheduling under uncertainty to a relaxed scheduling problem without uncertainty. |
Moshe Babaioff; Ronny Lempel; Brendan Lucier; Ishai Menache; Aleksandrs Slivkins; Sam Chiu-wai Wong; |
| 19 | The Parity Ray Regularizer for Pacing in Auction Markets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a novel regularizer which can describe those distributional preferences, while keeping the problem tractable. |
Andrea Celli; Riccardo Colini-Baldeschi; Christian Kroer; Eric Sodomka; |
| 20 | Auction Design in An Auto-bidding Setting: Randomization Improves Efficiency Beyond VCG Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present a prior-free randomized auction in which the PoA is approx. 1.896 for the case of two bidders, proving that one can achieve an efficiency strictly better than that under VCG in this setting. |
Aranyak Mehta; |
| 21 | Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we observe that economic interactions (competition among demand and supply) lead to statistical phenomenon (biased estimates). We develop a simple, tractable market model to study bias and variance in these experiments with interference. |
Hannah Li; Geng Zhao; Ramesh Johari; Gabriel Y. Weintraub; |
| 22 | Learning Explicit User Interest Boundary for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address those issues, we innovatively introduce an auxiliary score bu for each user to represent the User Interest Boundary(UIB) and individually penalize samples that cross the boundary with pairwise paradigms, i.e., the positive samples whose score is lower than bu and the negative samples whose score is higher than bu. |
Jianhuan Zhuo; Qiannan Zhu; Yinliang Yue; Yuhong Zhao; |
| 23 | Modeling User Behavior with Graph Convolution for Personalized Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. |
Lu Fan; Qimai Li; Bo Liu; Xiao-Ming Wu; Xiaotong Zhang; Fuyu Lv; Guli Lin; Sen Li; Taiwei Jin; Keping Yang; |
| 24 | Global or Local: Constructing Personalized Click Models for Web Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To shed light on this research question, we propose a Click Model Personalization framework (CMP) that adaptively selects from global and local models for individual users. |
Junqi Zhang; Yiqun Liu; Jiaxin Mao; Xiaohui Xie; Min Zhang; Shaoping Ma; Qi Tian; |
| 25 | A Model-Agnostic Causal Learning Framework for Recommendation Using Search Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing RSs mostly ignored the striking differences between the causal parts and non-causal parts when using these embedding vectors. In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results. |
Zihua Si; Xueran Han; Xiao Zhang; Jun Xu; Yue Yin; Yang Song; Ji-Rong Wen; |
| 26 | CoSimHeat: An Effective Heat Kernel Similarity Measure Based on Billion-Scale Network Topology✱ Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose CoSimHeat, a novel scalable graph-theoretic similarity model based on heat diffusion. |
Weiren Yu; Jian Yang; Maoyin Zhang; Di Wu; |
| 27 | Implicit User Awareness Modeling Via Candidate Items for CTR Prediction in Search Ads Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Instead, in this paper, we study the problem of modeling implicit user awareness about relevant/competing items. |
Kaifu Zheng; Lu Wang; Yu Li; Xusong Chen; Hu Liu; Jing Lu; Xiwei Zhao; Changping Peng; Zhangang Lin; Jingping Shao; |
| 28 | IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Collaborative signal is quite helpful for generating high-quality representation, exploiting which would benefit the representation learning of one node from its connected nodes. To tackle this limitation, in this work, we propose a new model IHGNN for personalized product search. |
Dian Cheng; Jiawei Chen; Wenjun Peng; Wenqin Ye; Fuyu Lv; Tao Zhuang; Xiaoyi Zeng; Xiangnan He; |
| 29 | Efficient Neural Ranking Using Forward Indexes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the Fast-Forward index – a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores – as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search. |
Jurek Leonhardt; Koustav Rudra; Megha Khosla; Abhijit Anand; Avishek Anand; |
| 30 | A Gain-Tuning Dynamic Negative Sampler for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The limitation of the false negative noise amplification may lead to overfitting and further poor generalization of the model. To address this issue, we propose a Gain-Tuning Dynamic Negative Sampling GDNS to make the recommendation more robust and effective. |
Qiannan Zhu; Haobo Zhang; Qing He; Zhicheng Dou; |
| 31 | Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, a major challenge is that the ANN index can be too large to fit into memory, given the considerable size of answer corpus. In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification. |
Shitao Xiao; Zheng Liu; Weihao Han; Jianjin Zhang; Yingxia Shao; Defu Lian; Chaozhuo Li; Hao Sun; Denvy Deng; Liangjie Zhang; Qi Zhang; Xing Xie; |
| 32 | FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing fairness-focused studies does not explicitly consider the problem of lacking negative feedback in implicit feedback data, while previous utility-focused methods ignore the importance of fairness in recommendations. To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data. |
Jie Li; Yongli Ren; Ke Deng; |
| 33 | Efficient and Effective Similarity Search Over Bipartite Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we first identify an inherent drawback of HPP and overcome it by proposing bidirectional HPP (BHPP). Then, we formulate similarity search over bipartite graphs as the problem of approximate BHPP computation, and present an efficient solution Approx-BHPP. |
Renchi Yang; |
| 34 | Preferences on A Budget: Prioritizing Document Pairs When Crowdsourcing Relevance Judgments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, following a similar idea to pooling strategies for single document relevance judgments where the goal is to sample the most useful documents to be judged, in this work we focus on analyzing alternative ways to sample document pairs to judge, in order to maximize the value of a fixed number of preference judgments that can feasibly be collected. |
Kevin Roitero; Alessandro Checco; Stefano Mizzaro; Gianluca Demartini; |
| 35 | Optimizing Rankings for Recommendation in Matching Markets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While this holds potential for improving market fluidity and fairness, we show in this paper that naively applying existing recommender systems to matching markets is sub-optimal. Considering the standard process where candidates apply and then get evaluated by employers, we present a new recommendation framework to model this interaction mechanism and propose efficient algorithms for computing personalized rankings in this setting. |
Yi Su; Magd Bayoumi; Thorsten Joachims; |
| 36 | Socialformer: Social Network Inspired Long Document Modeling for Document Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the model Socialformer, which introduces the characteristics of social networks into designing sparse attention patterns for long document modeling in document ranking. |
Yujia Zhou; Zhicheng Dou; Huaying Yuan; Zhengyi Ma; |
| 37 | PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In response to the above problems, we propose a pretrained network modulation and task adaptation approach (PNMTA) for user cold-start recommendation. |
Haoyu Pang; Fausto Giunchiglia; Ximing Li; Renchu Guan; Xiaoyue Feng; |
| 38 | A Category-aware Multi-interest Model for Personalized Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user’s multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. |
Jiongnan Liu; Zhicheng Dou; Qiannan Zhu; Ji-Rong Wen; |
| 39 | Asymptotically Unbiased Estimation for Delayed Feedback Modeling Via Label Correction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a new method, DElayed Feedback modeling with UnbiaSed Estimation, (DEFUSE), which aim to respectively correct the importance weights of the immediate positive, the fake negative, the real negative, and the delay positive samples at finer granularity. |
Yu Chen; Jiaqi Jin; Hui Zhao; Pengjie Wang; Guojun Liu; Jian Xu; Bo Zheng; |
| 40 | Towards A Better Understanding of Human Reading Comprehension with Brain Signals Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension. |
Ziyi Ye; Xiaohui Xie; Yiqun Liu; Zhihong Wang; Xuesong Chen; Min Zhang; Shaoping Ma; |
| 41 | ParClick: A Scalable Algorithm for EM-based Click Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we tackle the scalability of Expectation-Maximization (EM)-based click models by introducing ParClick, a new parallel algorithm designed by following the Partitioning-Communication-Aggregation-Mapping (PCAM) method. |
Pooya Khandel; Ilya Markov; Andrew Yates; Ana-Lucia Varbanescu; |
| 42 | Cross DQN: Cross Deep Q Network for Ads Allocation in Feed Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose Cross Deep Q Network (Cross DQN) to extract the crucial arrangement signal by crossing the embeddings of different items and modeling the crossed sequence by multi-channel attention. |
Guogang Liao; Ze Wang; Xiaoxu Wu; Xiaowen Shi; Chuheng Zhang; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 43 | CausPref: Causal Preference Learning for Out-of-Distribution Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. |
Yue He; Zimu Wang; Peng Cui; Hao Zou; Yafeng Zhang; Qiang Cui; Yong Jiang; |
| 44 | Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the problem, we propose a novel recommendation method named Deep Interest Highlight Network (DIHN) for Click-Through Rate (CTR) prediction in TIR scenarios. |
Qijie Shen; Hong Wen; Wanjie Tao; Jing Zhang; Fuyu Lv; Zulong Chen; Zhao Li; |
| 45 | Learning Neural Ranking Models Online from Implicit User Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, to unleash the power of representation learning in OL2R, we propose to directly learn a neural ranking model from users’ implicit feedback (e.g., clicks) collected on the fly. |
Yiling Jia; Hongning Wang; |
| 46 | StruBERT: Structure-aware BERT for Table Search and Matching Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table. |
Mohamed Trabelsi; Zhiyu Chen; Shuo Zhang; Brian D. Davison; Jeff Heflin; |
| 47 | Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, there are currently no sparse matrix techniques specifically designed for the sparsity structure one encounters in tree-based models for extreme multi-label ranking/classification (XMR/XMC) problems. To address this issue, we present the masked sparse chunk multiplication (MSCM) technique, a sparse matrix technique specifically tailored to XMR trees. |
Philip A. Etter; Kai Zhong; Hsiang-Fu Yu; Lexing Ying; Inderjit Dhillon; |
| 48 | RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore a new problem named temporal event forecasting, a generalized user behavior prediction task in a unified query product evolutionary graph, to embrace both query and product recommendation in a temporal manner. |
Ruijie Wang; Zheng Li; Danqing Zhang; Qingyu Yin; Tong Zhao; Bing Yin; Tarek Abdelzaher; |
| 49 | Learning Probabilistic Box Embeddings for Effective and Efficient Ranking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For efficiency, we propose a box embedding-based indexing method, which can safely filter irrelevant items and reduce the retrieval latency. |
Lang Mei; Jiaxin Mao; Gang Guo; Ji-Rong Wen; |
| 50 | Knowledge-aware Conversational Preference Elicitation with Bandit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a knowledge-aware conversational preference elicitation framework and a bandit-based algorithm GraphConUCB. |
Canzhe Zhao; Tong Yu; Zhihui Xie; Shuai Li; |
| 51 | A Multi-task Learning Framework for Product Ranking with BERT Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel end-to-end multi-task learning framework for product ranking with BERT to address the above challenges. |
Xuyang Wu; Alessandro Magnani; Suthee Chaidaroon; Ajit Puthenputhussery; Ciya Liao; Yi Fang; |
| 52 | Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we show two approaches for improving the performance of BERT-based bi-encoders. |
Euna Jung; Jaekeol Choi; Wonjong Rhee; |
| 53 | Am I A Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs Under Deepfake Impersonation Attack Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: By conducting a case study on celebrity face recognition, we examine the robustness of black-box commercial face recognition web APIs and open-source tools against Deepfake Impersonation (DI) attacks. |
Shahroz Tariq; Sowon Jeon; Simon S. Woo; |
| 54 | Game of Hide-and-Seek: Exposing Hidden Interfaces in Embedded Web Applications of IoT Devices Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present, a new approach that automatically exposes hidden web interfaces of IoT devices. |
Wei Xie; Jiongyi Chen; Zhenhua Wang; Chao Feng; Enze Wang; Yifei Gao; Baosheng Wang; Kai Lu; |
| 55 | Reproducibility and Replicability of Web Measurement Studies Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we survey 117 recent research papers to derive best practices for Web-based measurement studies and specify criteria that need to be met in practice. |
Nurullah Demir; Matteo Große-Kampmann; Tobias Urban; Christian Wressnegger; Thorsten Holz; Norbert Pohlmann; |
| 56 | Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots Via Multi-Agent Hierarchical Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This begs a question can adversaries, controlling socialbots, exploit AI techniques to their advantage? To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected. |
Thai Le; Long Tran-Thanh; Dongwon Lee; |
| 57 | A View Into YouTube View Fraud Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we explore organic or human-driven approaches to view fraud, conducting a case study on a long-running YouTube view fraud campaign operated on a popular free video streaming service, 123Movies. |
Dhruv Kuchhal; Frank Li; |
| 58 | Et Tu, Brute? Privacy Analysis of Government Websites and Mobile Apps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we perform privacy and security measurements on government websites and Android apps: 150,244 unique websites (from 206 countries) and 1166 Android apps (from 71 countries). |
Nayanamana Samarasinghe; Aashish Adhikari; Mohammad Mannan; Amr Youssef; |
| 59 | Investigating Advertisers’ Domain-changing Behaviors and Their Impacts on Ad-blocker Filter Lists Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we proposed methods to discover RAD domains and categorized their change patterns. |
Su-Chin Lin; Kai-Hsiang Chou; Yen Chen; Hsu-Chun Hsiao; Darion Cassel; Lujo Bauer; Limin Jia; |
| 60 | Verba Volant, Scripta Volant: Understanding Post-publication Title Changes in News Outlets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we collect and analyze over 41K pairs of altered news headlines by tracking ? 411K articles from major US news agencies over a six month period (March to September 2021), identifying that 7.5% articles have at least one post-publication headline edit with a wide range of types, from minor updates, to complete rewrites. |
Xingzhi Guo; Brian Kondracki; Nick Nikiforakis; Steven Skiena; |
| 61 | Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints. |
Zhongzheng Xiong; Jialin Sun; Xiaojun Mao; Jian Wang; Ying Shan; Zengfeng Huang; |
| 62 | MemStream: Memory-Based Streaming Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, we need a data-efficient method that can detect and adapt to changing data trends, or concept drift, in an online manner. In this work, we propose MemStream, a streaming anomaly detection framework, allowing us to detect unusual events as they occur while being resilient to concept drift. |
Siddharth Bhatia; Arjit Jain; Shivin Srivastava; Kenji Kawaguchi; Bryan Hooi; |
| 63 | Federated Unlearning Via Class-Discriminative Pruning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Through the visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we propose a method for scrubbing the model cleanly of information about particular categories. |
Junxiao Wang; Song Guo; Xin Xie; Heng Qi; |
| 64 | ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data. |
Xinjie Lin; Gang Xiong; Gaopeng Gou; Zhen Li; Junzheng Shi; Jing Yu; |
| 65 | Attention-Based Vandalism Detection in OpenStreetMap Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents Ovid – a novel attention-based method for vandalism detection in OSM. |
Nicolas Tempelmeier; Elena Demidova; |
| 66 | CoProtector: Protect Open-Source Code Against Unauthorized Training Usage with Data Poisoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To mitigate such impacts, we argue that there is a need to invent effective mechanisms for protecting open-source code from being exploited by deep learning models. Here, we design and implement a prototype, CoProtector, which utilizes data poisoning techniques to arm source code repositories for defending against such exploits. |
Zhensu Sun; Xiaoning Du; Fu Song; Mingze Ni; Li Li; |
| 67 | TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum. |
Sijia Li; Gaopeng Gou; Chang Liu; Chengshang Hou; Zhenzhen Li; Gang Xiong; |
| 68 | Measuring Alexa Skill Privacy Practices Across Three Years Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we perform a systematic and longitudinal measurement study of the Alexa marketplace. |
Jide Edu; Xavier Ferrer-Aran; Jose Such; Guillermo Suarez-Tangil; |
| 69 | Revisiting Email Forwarding Security Under The Authenticated Received Chain Protocol Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we perform an empirical analysis on ARC usage and examine how it affects spoofing detection decisions on popular email provides that support ARC. |
Chenkai Wang; Gang Wang; |
| 70 | ALLIE: Active Learning on Large-scale Imbalanced Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These assumptions fail in extreme rare-class classification scenarios, such as classifying abusive reviews in an e-commerce website. We propose a novel framework ALLIE to address this challenge of active learning in large-scale imbalanced graph data. |
Limeng Cui; Xianfeng Tang; Sumeet Katariya; Nikhil Rao; Pallav Agrawal; Karthik Subbian; Dongwon Lee; |
| 71 | Beyond Bot Detection: Combating Fraudulent Online Survey Takers✱ Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we conduct an empirical evaluation of 22 anti-fraud tests in two complementary online surveys. |
Ziyi Zhang; Shuofei Zhu; Jaron Mink; Aiping Xiong; Linhai Song; Gang Wang; |
| 72 | Measuring The Privacy Vs. Compatibility Trade-off in Preventing Third-Party Stateful Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our contributions include web-scale measurements of page behaviors under multiple third-party storage policies inspired by production browsers. |
Jordan Jueckstock; Peter Snyder; Shaown Sarker; Alexandros Kapravelos; Benjamin Livshits; |
| 73 | DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational Autoencoders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we tackle anonymization of textual data and propose an end-to-end differentially private variational autoencoder architecture. |
Benjamin Weggenmann; Valentin Rublack; Michael Andrejczuk; Justus Mattern; Florian Kerschbaum; |
| 74 | An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee). To overcome this issue, we propose an efficient model perturbation method for federated learning to defend against reconstruction and membership inference attacks launched by curious clients. |
Xue Yang; Yan Feng; Weijun Fang; Jun Shao; Xiaohu Tang; Shu-Tao Xia; Rongxing Lu; |
| 75 | Link: Black-Box Detection of Cross-Site Scripting Vulnerabilities Using Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present Link, a general RL framework whose states, actions, and a reward function are designed to find reflected XSS vulnerabilities in a black-box and fully automatic manner. |
Soyoung Lee; Seongil Wi; Sooel Son; |
| 76 | HiddenCPG: Large-Scale Vulnerable Clone Detection Using Subgraph Isomorphism of Code Property Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose, a scalable system designed to identify various web vulnerabilities, including bugs that stem from incorrect sanitization. |
Seongil Wi; Sijae Woo; Joyce Jiyoung Whang; Sooel Son; |
| 77 | Understanding The Practice of Security Patch Management Across Multiple Branches in OSS Projects Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the rapidly growing number of OSS vulnerabilities has greatly strained this patch deployment model, and a critical need has arisen for the security community to understand the practice of security patch management across stable branches. In this work, we conduct a large-scale empirical study of stable branches in OSS projects and the security patches deployed on them via investigating 608 stable branches belonging to 26 popular OSS projects as well as more than 2,000 security fixes for 806 CVEs deployed on stable branches. |
Xin Tan; Yuan Zhang; Jiajun Cao; Kun Sun; Mi Zhang; Min Yang; |
| 78 | Ontology-enhanced Prompt-tuning for Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we explore knowledge injection for FSL with pre-trained language models and propose ontology-enhanced prompt-tuning (OntoPrompt). |
Hongbin Ye; Ningyu Zhang; Shumin Deng; Xiang Chen; Hui Chen; Feiyu Xiong; Xi Chen; Huajun Chen; |
| 79 | Time-aware Entity Alignment Using Temporal Relational Attention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a novel Temporal Relational Entity Alignment method (TREA) which is able to learn alignment-oriented TKG embeddings and represent new emerging entities. |
Chengjin Xu; Fenglong Su; Bo Xiong; Jens Lehmann; |
| 80 | Rethinking Graph Convolutional Networks in Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This phenomenon motivates us to explore the real effect of GCNs in KGC. Therefore, in this paper, we build upon representative GCN-based KGC models and introduce variants to find which factor of GCNs is critical in KGC. |
Zhanqiu Zhang; Jie Wang; Jieping Ye; Feng Wu; |
| 81 | Creating Signature-Based Views for Description Logic Ontologies with Transitivity and Qualified Number Restrictions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In particular, we develop a novel and practical uniform interpolation method for creating signature-based views for ontologies specified in the description logic , a very expressive description logic for which uniform interpolation has not been fully addressed. |
Yue Xiang; Xuan Wu; Chang Lu; Yizheng Zhao; |
| 82 | EvoLearner: Learning Description Logics with Evolutionary Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose EvoLearner—an evolutionary approach to learn concepts in , which is the attributive language with complement () paired with qualified cardinality restrictions () and data properties (). |
Stefan Heindorf; Lukas Blübaum; Nick Düsterhus; Till Werner; Varun Nandkumar Golani; Caglar Demir; Axel-Cyrille Ngonga Ngomo; |
| 83 | Uncertainty-aware Pseudo Label Refinery for Entity Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, a simple but effective Uncertainty-aware Pseudo Label Refinery (UPLR) framework is proposed without manually labeling requirement and is capable of learning high-quality entity embeddings from pseudo-labeled data sets containing noisy data. |
Jia Li; Dandan Song; |
| 84 | Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the latest findings in the field of Contrastive Learning, we propose a novel KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE). |
Kai Wang; Yu Liu; Quan Z. Sheng; |
| 85 | EventBERT: A Pre-Trained Model for Event Correlation Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose EventBERT, a pre-trained model to encapsulate eventuality knowledge from unlabeled text. |
Yucheng Zhou; Xiubo Geng; Tao Shen; Guodong Long; Daxin Jiang; |
| 86 | SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision. |
Xiao Liu; Haoyun Hong; Xinghao Wang; Zeyi Chen; Evgeny Kharlamov; Yuxiao Dong; Jie Tang; |
| 87 | Unified Question Generation with Continual Lifelong Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats. |
Wei Yuan; Hongzhi Yin; Tieke He; Tong Chen; Qiufeng Wang; Lizhen Cui; |
| 88 | What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We introduce a human-in-the-loop, semantic analysis framework for characterizing unknown unknowns at scale. |
Shahin Sharifi Noorian; Sihang Qiu; Ujwal Gadiraju; Jie Yang; Alessandro Bozzon; |
| 89 | Enhancing Knowledge Bases with Quantity Facts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Prior work on extracting quantity facts from web contents focused on high precision for top-ranked outputs, but did not tackle the KB coverage issue. This paper presents a recall-oriented approach which aims to close this gap in knowledge-base coverage. |
Vinh Thinh Ho; Daria Stepanova; Dragan Milchevski; Jannik Strötgen; Gerhard Weikum; |
| 90 | Translating Place-Related Questions to GeoSPARQL Queries Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we first present an enhanced version of YAGO2geo, the geospatially-enabled variant of the YAGO2 knowledge base, by linking and adding more than one million places from OpenStreetMap data to YAGO2. |
Ehsan Hamzei; Martin Tomko; Stephan Winter; |
| 91 | Knowledge Graph Reasoning with Relational Digraph Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce a novel relational structure, i.e., relational directed graph (r-digraph), which is composed of overlapped relational paths, to capture the KG’s local evidence. |
Yongqi Zhang; Quanming Yao; |
| 92 | TaxoEnrich: Self-Supervised Taxonomy Completion Via Structure-Semantic Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose TaxoEnrich, a new taxonomy completion framework, which effectively leverages both semantic features and structural information in the existing taxonomy and offers a better representation of candidate position to boost the performance of taxonomy completion. |
Minhao Jiang; Xiangchen Song; Jieyu Zhang; Jiawei Han; |
| 93 | Conditional Generation Net for Medication Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose the Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. |
Rui Wu; Zhaopeng Qiu; Jiacheng Jiang; Guilin Qi; Xian Wu; |
| 94 | Path Language Modeling Over Knowledge Graphsfor Explainable Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Hence, previous approaches are constrained by a recall bias in terms of existing connectivity of KG structures. To overcome this, we propose a novel Path Language Modeling Recommendation (PLM-Rec) framework, learning a language model over KG paths consisting of entities and edges. |
Shijie Geng; Zuohui Fu; Juntao Tan; Yingqiang Ge; Gerard de Melo; Yongfeng Zhang; |
| 95 | Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. |
Jiacheng Huang; Yao Zhao; Wei Hu; Zhen Ning; Qijin Chen; Xiaoxia Qiu; Chengfu Huo; Weijun Ren; |
| 96 | What’s in An Index: Extracting Domain-specific Knowledge Graphs from Textbooks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we extend our previous work on extraction of knowledge models from digital textbooks. |
Isaac Alpizar-Chacon; Sergey Sosnovsky; |
| 97 | Can Machine Translation Be A Reasonable Alternative for Multilingual Question Answering Systems Over Knowledge Graphs? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we discuss Knowledge Graph Question Answering (KGQA) systems that aim at providing natural language access to data stored in Knowledge Graphs (KG). |
Aleksandr Perevalov; Andreas Both; Dennis Diefenbach; Axel-Cyrille Ngonga Ngomo; |
| 98 | AR-BERT: Aspect-relation Enhanced Aspect-level Sentiment Classification with Multi-modal Explanations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models. |
Sk Mainul Islam; Sourangshu Bhattacharya; |
| 99 | Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Recent work has explored methods for adapting PLMs to downstream tasks while keeping most (or all) of their parameters frozen. We examine two such promising techniques, prefix tuning and bias-term tuning, specifically on semantic parsing. |
Weiqi Sun; Haidar Khan; Nicolas Guenon des Mesnards; Melanie Rubino; Konstantine Arkoudas; |
| 100 | QEN: Applicable Taxonomy Completion Via Evaluating Full Taxonomic Relations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the above drawbacks, we propose the Quadruple Evaluation Network (QEN), a novel taxonomy completion framework that utilizes easily accessible term descriptions as input, and applies pretrained language model and code attention for accurate inference while reducing online computation. |
Suyuchen Wang; Ruihui Zhao; Yefeng Zheng; Bang Liu; |
| 101 | Learning and Evaluating Graph Neural Network Explanations Based on Counterfactual and Factual Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we take insights of Counterfactual and Factual (CF2) reasoning from causal inference theory, to solve both the learning and evaluation problems in explainable GNNs. |
Juntao Tan; Shijie Geng; Zuohui Fu; Yingqiang Ge; Shuyuan Xu; Yunqi Li; Yongfeng Zhang; |
| 102 | Exploring Edge Disentanglement for Node Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, with close examination of edge patterns, we propose three heuristics and design three corresponding pretext tasks to guide the automatic edge disentanglement. |
Tianxiang Zhao; Xiang Zhang; Suhang Wang; |
| 103 | Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in The UMLS Metathesaurus Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop multiple variants of context-enriched learning models (ConLMs) by adding to the LexLM the types of contextual information listed above. |
Vinh Nguyen; Hong Yung Yip; Goonmeet Bajaj; Thilini Wijesiriwardene; Vishesh Javangula; Srinivasan Parthasarathy; Amit Sheth; Olivier Bodenreider; |
| 104 | Federated SPARQL Query Processing Over Heterogeneous Linked Data Fragments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. |
Lars Heling; Maribel Acosta; |
| 105 | An Invertible Graph Diffusion Neural Network for Source Localization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper aims to establish a generic framework of invertible graph diffusion models for source localization on graphs, namely Invertible Validity-aware Graph Diffusion (IVGD), to handle major challenges including 1) Difficulty to leverage knowledge in graph diffusion models for modeling their inverse processes in an end-to-end fashion, 2) Difficulty to ensure the validity of the inferred sources, and 3) Efficiency and scalability in source inference. |
Junxiang Wang; Junji Jiang; Liang Zhao; |
| 106 | SimGRACE: A Simple Framework for Graph Contrastive Learning Without Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: All of these limit the efficiency and more general applicability of existing GCL methods. To circumvent these crucial issues, we propose a Simple framework for GRAph Contrastive lEarning, SimGRACE for brevity, which does not require data augmentations. |
Jun Xia; Lirong Wu; Jintao Chen; Bozhen Hu; Stan Z. Li; |
| 107 | MiDaS: Representative Sampling from Real-world Hypergraphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by the prevalence of large-scale hypergraphs, we study the problem of representative sampling from real-world hypergraphs, aiming to answer (Q1) what a representative sub-hypergraph is and (Q2) how we can find a representative one rapidly without an extensive search. |
Minyoung Choe; Jaemin Yoo; Geon Lee; Woonsung Baek; U Kang; Kijung Shin; |
| 108 | A New Dynamic Algorithm for Densest Subhypergraphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This algorithm worked only on unweighted hypergraphs, and had an approximation ratio of (1 +?)r2 and an update time of O(poly(r, log?n)), where r denotes the maximum rank of the input across all the updates. We obtain a new algorithm for this problem, which works even when the input hypergraph is weighted. |
Suman K. Bera; Sayan Bhattacharya; Jayesh Choudhari; Prantar Ghosh; |
| 109 | Graph Alignment with Noisy Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, it is difficult to find an abiding threshold to separate the potential positive (benign) and negative (noisy) data in the whole training process. To address these important issues, in this paper, we design a non-sampling discrimination model resorting to the unbiased risk estimation of positive-unlabeled learning to circumvent the harmful impact of negative sampling. |
Shichao Pei; Lu Yu; Guoxian Yu; Xiangliang Zhang; |
| 110 | CGC: Contrastive Graph Clustering ForCommunity Detection and Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clustering perspective. |
Namyong Park; Ryan Rossi; Eunyee Koh; Iftikhar Ahamath Burhanuddin; Sungchul Kim; Fan Du; Nesreen Ahmed; Christos Faloutsos; |
| 111 | Graph Neural Networks Beyond Compromise Between Attribute and Topology Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although existing Graph Neural Networks (GNNs) based on message passing achieve state-of-the-art, the over-smoothing issue, node similarity distortion issue and dissatisfactory link prediction performance can’t be ignored. This paper summarizes these issues as the interference between topology and attribute for the first time. |
Liang Yang; Wenmiao Zhou; Weihang Peng; Bingxin Niu; Junhua Gu; Chuan Wang; Xiaochun Cao; Dongxiao He; |
| 112 | Graph Sanitation with Application to Node Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce the graph sanitation problem, to answer an orthogonal question. |
Zhe Xu; Boxin Du; Hanghang Tong; |
| 113 | Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To solve these problems, we formulate social context based fake news detection as a heterogeneous graph classification problem, and propose a fake news detection model named Post-User Interaction Network (PSIN), which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics. |
Erxue Min; Yu Rong; Yatao Bian; Tingyang Xu; Peilin Zhao; Junzhou Huang; Sophia Ananiadou; |
| 114 | TREND: TempoRal Event and Node Dynamics for Graph Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). |
Zhihao Wen; Yuan Fang; |
| 115 | Resource-Efficient Training for Large Graph Convolutional Networks with Label-Centric Cumulative Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we argue that a GCN can be trained with a sampled subgraph to produce approximate node representations, which inspires us a novel perspective to accelerate GCN training via network sampling. |
Mingkai Lin; Wenzhong Li; Ding Li; Yizhou Chen; Sanglu Lu; |
| 116 | Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem. |
Weiming Liu; Xiaolin Zheng; Mengling Hu; Chaochao Chen; |
| 117 | Lightning Fast and Space Efficient K-clique Counting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Computing the count of k-cliques in a graph for a large k (e.g., k = 8) is often intractable as the number of k-cliques increases exponentially w.r.t. (with respect to) k. Existing exact k-clique counting algorithms are often hard to handle large dense graphs, while sampling-based solutions either require a huge number of samples or consume very high storage space to achieve a satisfactory accuracy. To overcome these limitations, we propose a new framework to estimate the number of k-cliques which integrates both the exact k-clique counting technique and two novel color-based sampling techniques. |
Xiaowei Ye; Rong-Hua Li; Qiangqiang Dai; Hongzhi Chen; Guoren Wang; |
| 118 | Graph Communal Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar. To address this issue, we propose a novel Graph Communal Contrastive Learning (gCooL) framework to jointly learn the community partition and learn node representations in an end-to-end fashion. |
Bolian Li; Baoyu Jing; Hanghang Tong; |
| 119 | RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We formulate the problem of mitigating the degree-related performance disparity in GCN from the perspective of the Rawlsian difference principle, which is originated from the theory of distributive justice. |
Jian Kang; Yan Zhu; Yinglong Xia; Jiebo Luo; Hanghang Tong; |
| 120 | Geometric Graph Representation Learning Via Maximizing Rate Reduction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Such pair-wise learning schemes could fail to capture the global distribution of representations, since it has no explicit constraints on the global geometric properties of representation space. To this end, we propose Geometric Graph Representation Learning (G2R) to learn node representations in an unsupervised manner via maximizing rate reduction. |
Xiaotian Han; Zhimeng Jiang; Ninghao Liu; Qingquan Song; Jundong Li; Xia Hu; |
| 121 | Dual Space Graph Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel graph contrastive learning method, namely Dual Space Graph Contrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. |
Haoran Yang; Hongxu Chen; Shirui Pan; Lin Li; Philip S. Yu; Guandong Xu; |
| 122 | Confidence May Cheat: Self-Training on Graph Neural Networks Under Distribution Shift Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Surprisingly, we find that high-confidence unlabeled nodes are not always useful, and even introduce the distribution shift issue between the original labeled dataset and the augmented dataset by self-training, severely hindering the capability of self-training on graphs. To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset. |
Hongrui Liu; Binbin Hu; Xiao Wang; Chuan Shi; Zhiqiang Zhang; Jun Zhou; |
| 123 | EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Different from existing works that debias GNN models, we aim to debias the input attributed network to achieve fairer GNNs through feeding GNNs with less biased data. |
Yushun Dong; Ninghao Liu; Brian Jalaian; Jundong Li; |
| 124 | Meta-Weight Graph Neural Network: Push The Limits Beyond Global Homophily Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, many existing GNNs integrate node features and structure identically, which ignores the distributions of nodes and further limits the expressive power of GNNs. To solve these problems, we propose Meta Weight Graph Neural Network (MWGNN) to adaptively construct graph convolution layers for different nodes. |
Xiaojun Ma; Qin Chen; Yuanyi Ren; Guojie Song; Liang Wang; |
| 125 | Model-Agnostic Augmentation for Accurate Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we introduce five desired properties for effective augmentation. |
Jaemin Yoo; Sooyeon Shim; U Kang; |
| 126 | Multimodal Continual Graph Learning with Neural Architecture Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, considering multimodal continual graph learning with evolving topological structures poses great challenges: i) it is unclear how to incorporate the multimodal information into continual graph learning and ii) it is nontrivial to design models that can capture the structure-evolving dynamics in continual graph learning. To tackle these challenges, in this paper we propose a novel Multimodal Structure-evolving Continual Graph Learning (MSCGL) model, which continually learns both the model architecture and the corresponding parameters for Adaptive Multimodal Graph Neural Network (AdaMGNN). |
Jie Cai; Xin Wang; Chaoyu Guan; Yateng Tang; Jin Xu; Bin Zhong; Wenwu Zhu; |
| 127 | KoMen: Domain Knowledge Guided Interaction Recommendation for Emerging Scenarios Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: There are two challenges in these emerging scenarios: (1) The behavior of users on the emerging scenarios could be different from existing ones due to the diversity among scenarios; (2) Emerging scenarios may only have scarce user behavioral data for model learning. Towards these two challenges, we present KoMen, a Domain Knowledge Guided Meta-learning framework for Interaction Recommendation. |
Yiqing Xie; Zhen Wang; Carl Yang; Yaliang Li; Bolin Ding; Hongbo Deng; Jiawei Han; |
| 128 | AUC-oriented Graph Neural Network for Fraud Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a model named AO-GNN (Short for AUC-oriented GNN), to achieve AUC maximization on GNN under the aforementioned framework. |
Mengda Huang; Yang Liu; Xiang Ao; Kuan Li; Jianfeng Chi; Jinghua Feng; Hao Yang; Qing He; |
| 129 | Unsupervised Graph Poisoning Attack Via Contrastive Loss Back-propagation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel unsupervised gradient-based adversarial attack that does not rely on labels for graph contrastive learning. |
Sixiao Zhang; Hongxu Chen; Xiangguo Sun; Yicong Li; Guandong Xu; |
| 130 | Graph-adaptive Rectified Linear Unit for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To make the updating function topology-aware, we inject the topological information into the non-linear activation function and propose Graph-adaptive Rectified Linear Unit (GReLU), which is a new parametric activation function incorporating the neighborhood information in a novel and efficient way. |
Yifei Zhang; Hao Zhu; Ziqiao Meng; Piotr Koniusz; Irwin King; |
| 131 | Neural Predicting Higher-order Patterns in Temporal Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we propose the first model, named HIT, for full-spectrum higher-order pattern prediction in temporal hypergraphs. |
Yunyu Liu; Jianzhu Ma; Pan Li; |
| 132 | Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a distributionally robust self-supervised graph neural network framework to learn the representations. |
Yanfu Zhang; Hongchang Gao; Jian Pei; Heng Huang; |
| 133 | Adversarial Graph Contrastive Learning with Information Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, by introducing an adversarial graph view and an information regularizer, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ArieL), to extract informative contrastive samples within a reasonable constraint. |
Shengyu Feng; Baoyu Jing; Yada Zhu; Hanghang Tong; |
| 134 | A Rapid Source Localization Method in The Early Stage of Large-scale Network Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Therefore, it is of great significance to localize diffusion sources as early as possible to stem the spread of malicious information. This paper proposes a novel sensor-based method, called greedy full-order neighbor localization (denoted as GFNL), to solve this problem under a low infection propagation in line with the real world. |
Zhen Wang; Dongpeng Hou; Chao Gao; Jiajin Huang; Qi Xuan; |
| 135 | Designing The Topology of Graph Neural Networks: A Novel Feature Fusion Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To be specific, we provide a feature fusion perspective in designing GNN topology and propose a novel framework to unify the existing topology designs with feature selection and fusion strategies. |
Lanning Wei; Huan Zhao; Zhiqiang He; |
| 136 | Towards Unsupervised Deep Graph Structure Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a more practical GSL paradigm, unsupervised graph structure learning, where the learned graph topology is optimized by data itself without any external guidance (i.e., labels). |
Yixin Liu; Yu Zheng; Daokun Zhang; Hongxu Chen; Hao Peng; Shirui Pan; |
| 137 | Polarized Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Inspired by the idea of attitude polarization in social psychology, that people tend to be more extreme when exposed to an opposite opinion, we propose Polarized Graph Neural Network (Polar-GNN). |
Zheng Fang; Lingjun Xu; Guojie Song; Qingqing Long; Yingxue Zhang; |
| 138 | Fair K-Center Clustering in MapReduce and Streaming Settings Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study the problem of fairness aware k-center clustering over large datasets. |
Suman K. Bera; Syamantak Das; Sainyam Galhotra; Sagar Sudhir Kale; |
| 139 | Unbiased Graph Embedding with Biased Graph Observations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes. Motivated by this new perspective, we propose two complementary methods for uncovering such an underlying graph, with the goal of introducing minimum impact on the utility of the embeddings. |
Nan Wang; Lu Lin; Jundong Li; Hongning Wang; |
| 140 | Prohibited Item Detection Via Risk Graph Structure Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work proposes the Risk Graph Structure Learning model (RGSL) for prohibited item detection. |
Yugang Ji; Guanyi Chu; Xiao Wang; Chuan Shi; Jianan Zhao; Junping Du; |
| 141 | FreSCo: Mining Frequent Patterns in Simplicial Complexes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce simplicial patterns —that we call simplets— and generalize the task of frequent pattern mining from the realm of graphs to that of simplicial complexes. |
Giulia Preti; Gianmarco De Francisci Morales; Francesco Bonchi; |
| 142 | Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, most existing CDR models assume information can directly ‘transfer across the bridge’, ignoring the privacy issues. To solve this problem, we propose a novel two stage based privacy-preserving CDR framework (PriCDR). |
Chaochao Chen; Huiwen Wu; Jiajie Su; Lingjuan Lyu; Xiaolin Zheng; Li Wang; |
| 143 | Inflation Improves Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This noise increases more severely as the layers of GNNs deepen, which is also a main reason of over-smoothing. In this paper, we propose a new convolution strategy for GNNs to address this problem via suppressing the noise propagation. |
Dongxiao He; Rui Guo; Xiaobao Wang; Di Jin; Yuxiao Huang; Wenjun Wang; |
| 144 | Generating Simple Directed Social Network Graphs for Information Spreading Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an approach to generate directed social network graphs that creates reciprocal and directed edges and considers the correlation between the respective degree sequences. |
Christoph Schweimer; Christine Gfrerer; Florian Lugstein; David Pape; Jan A. Velimsky; Robert Elsässer; Bernhard C. Geiger; |
| 145 | H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the existing GNN-based fraud detection methods just enhance the homophily in fraud graph and use the low-pass filter to retain the commonality of node features among the neighbors, which inevitably ignore the difference among neighbor of heterophilic connections. To address this problem, we propose a Graph Neural Network-based Fraud Detector with Homophilic and Heterophilic Interactions (H2-FDetector for short). |
Fengzhao Shi; Yanan Cao; Yanmin Shang; Yuchen Zhou; Chuan Zhou; Jia Wu; |
| 146 | SATMargin: Practical Maximal Frequent Subgraph Mining Via Margin Space Sampling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here, we propose a practical MFS mining algorithm that targets large MFSs, named SATMargin. |
Muyi Liu; Pan Li; |
| 147 | On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a novel graph neural network named SOLT-GNN, to close the representational gap between the head and tail graphs from the perspective of knowledge transfer. |
Zemin Liu; Qiheng Mao; Chenghao Liu; Yuan Fang; Jianling Sun; |
| 148 | Listing Maximal K-Plexes in Large Real-World Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we continue the research line of listing all maximal k-plexes and maximal k-plexes of prescribed size. |
Zhengren Wang; Yi Zhou; Mingyu Xiao; Bakhadyr Khoussainov; |
| 149 | Curvature Graph Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named CurvGAN, which is the first GAN-based graph representation method in the Riemannian geometric manifold. |
Jianxin Li; Xingcheng Fu; Qingyun Sun; Cheng Ji; Jiajun Tan; Jia Wu; Hao Peng; |
| 150 | Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on our findings, we propose better practices and sanity checks for future research and applications, including adhering to principles in visual CL when designing context-aware graph augmentations. |
Puja Trivedi; Ekdeep Singh Lubana; Yujun Yan; Yaoqing Yang; Danai Koutra; |
| 151 | GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel GNN model based on a bi-kernel feature transformation and a selection gate. |
Lun Du; Xiaozhou Shi; Qiang Fu; Xiaojun Ma; Hengyu Liu; Shi Han; Dongmei Zhang; |
| 152 | Learning The Markov Order of Paths in Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We address the problem of learning the Markov order in categorical sequences that represent paths in a network, i.e., sequences of variable lengths where transitions between states are constrained to a known graph. |
Luka V. Petrovic; Ingo Scholtes; |
| 153 | A Viral Marketing-Based Model For Opinion Dynamics in Online Social Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the interplay between these two aspects: opinion formation and information cascades in online social networks. |
Sijing Tu; Stefan Neumann; |
| 154 | ONBRA: Rigorous Estimation of The Temporal Betweenness Centrality in Temporal Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we present , the first sampling-based approximation algorithm for estimating the temporal betweenness centrality values of the nodes in a temporal network, providing rigorous probabilistic guarantees on the quality of its output. |
Diego Santoro; Ilie Sarpe; |
| 155 | FirmCore Decomposition of Multilayer Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present FirmCore, a new family of dense subgraphs in ML networks, and show that it satisfies many of the nice properties of k-cores in single-layer graphs. |
Farnoosh Hashemi; Ali Behrouz; Laks V.S. Lakshmanan; |
| 156 | Compact Graph Structure Learning Via Mutual Information Compression Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we theoretically prove that if we optimize basic views and final view based on mutual information, and keep their performance on labels simultaneously, the final view will be a minimal sufficient structure. |
Nian Liu; Xiao Wang; Lingfei Wu; Yu Chen; Xiaojie Guo; Chuan Shi; |
| 157 | ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This issue is also challenging when it couples with graph structures. To address this, we present the cluster-aware supervised contrastive learning loss (ClusterSCL1) for graph learning tasks. |
Yanling Wang; Jing Zhang; Haoyang Li; Yuxiao Dong; Hongzhi Yin; Cuiping Li; Hong Chen; |
| 158 | Graph Neural Network for Higher-Order Dependency Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This kind of sequential data from complex systems (including natural dependencies) are often ignored by existing GNNs which makes them ineffective. To address this problem, we propose for the first time new GNN approaches for higher-order networks in this paper. |
Di Jin; Yingli Gong; Zhiqiang Wang; Zhizhi Yu; Dongxiao He; Yuxiao Huang; Wenjun Wang; |
| 159 | Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: If each meta-path is treated as an isolated semantic data resource and the correlations among them are disregarded, sub-optimality in the both the meta-path based embedding and final embedding will be resulted. To address this issue, we make the first attempt to explicitly model the correlation among meta-paths by proposing Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding (CKD). |
Can Wang; Sheng Zhou; Kang Yu; Defang Chen; Bolang Li; Yan Feng; Chun Chen; |
| 160 | Temporal Walk Centrality: Ranking Nodes in Evolving Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose exact and approximation algorithms with different running times depending on the properties of the temporal network and parameters of our new centrality measure. |
Lutz Oettershagen; Petra Mutzel; Nils M. Kriege; |
| 161 | Dual-branch Density Ratio Estimation for Signed Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To alleviate the above-mentioned issues, in this paper, we propose a novel dual-branch density ratio estimation (DDRE) architecture for SNE. |
Pinghua Xu; Yibing Zhan; Liu Liu; Baosheng Yu; Bo Du; Jia Wu; Wenbin Hu; |
| 162 | Successful New-entry Prediction for Multi-Party Online Conversations Via Latent Topics and Discourse Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer’s message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction)1. |
Lingzhi Wang; Jing Li; Xingshan Zeng; Kam-Fai Wong; |
| 163 | This Must Be The Place: Predicting Engagement of Online Communities in A Large-scale Distributed Campaign Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work we study the behavior of thousands of communities with millions of active members. |
Abraham Israeli; Alexander Kremiansky; Oren Tsur; |
| 164 | The Influences of Task Design on Crowdsourced Judgement: A Case Study of Recidivism Risk Evaluation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, using recidivism risk evaluation as an example, we conduct a randomized experiment to examine the effects of two common designs of crowdsourcing judgement tasks—encouraging the crowd to deliberate and providing feedback to the crowd—on the quality, strictness, and fairness of the crowd’s recidivism risk judgements. |
Xiaoni Duan; Chien-Ju Ho; Ming Yin; |
| 165 | Will You Accept The AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we aim at obtaining a quantitative understanding of whether and when would human decision makers adopt the AI model’s recommendations. |
Xinru Wang; Zhuoran Lu; Ming Yin; |
| 166 | Ready Player One! Eliciting Diverse Knowledge Using A Configurable Game Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this end, collecting broad and tacit knowledge, in addition to negative or discriminative knowledge can be highly useful. Addressing this research gap, we developed a novel game with a purpose, ‘FindItOut’, to elicit different types of knowledge from human players through easily configurable game mechanics. |
Agathe Balayn; Gaole He; Andrea Hu; Jie Yang; Ujwal Gadiraju; |
| 167 | Measuring Annotator Agreement Generally Across Complex Structured, Multi-object, and Free-text Annotation Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We identify the difficulty of interpretability and the complexity of choosing a distance function as key obstacles in applying Krippendorff’s ? generally across these tasks. We propose two novel, more interpretable measures, showing they yield more consistent IAA measures across tasks and annotation distance functions. |
Alexander Braylan; Omar Alonso; Matthew Lease; |
| 168 | Capturing Diverse and Precise Reactions to A Comment with User-Generated Labels Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We explore user-generated labels (UGLs) as an alternative reaction design to capture the rich context of user reactions to comments. |
Eun-Young Ko; Eunseo Choi; Jeong-woo Jang; Juho Kim; |
| 169 | Accelerating Serverless Computing By Harvesting Idle Resources Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. |
Hanfei Yu; Hao Wang; Jian Li; Xu Yuan; Seung-Jong Park; |
| 170 | QCluster: Clustering Packets for Flow Scheduling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. |
Tong Yang; Jizhou Li; Yikai Zhao; Kaicheng Yang; Hao Wang; Jie Jiang; Yinda Zhang; Nicholas Zhang; |
| 171 | Modeling and Optimizing The Scaling Performance in Distributed Deep Learning Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a recursive model, OSF (Scaling Factor considering Overlap), for estimating the scaling performance of DDL training of neural network models, given the settings of the DDL system. |
Ting Liu; Tianhao Miao; Qinghua Wu; Zhenyu Li; Guangxin He; Jiaoren Wu; Shengzhuo Zhang; Xingwu Yang; Gareth Tyson; Gaogang Xie; |
| 172 | Fograph: Enabling Real-Time Deep Graph Inference with Fog Computing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse resources of multiple fog nodes in proximity to IoT data sources. |
Liekang Zeng; Peng Huang; Ke Luo; Xiaoxi Zhang; Zhi Zhou; Xu Chen; |
| 173 | Robust System Instance Clustering for Large-Scale Web Services Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose OmniCluster to accurately and efficiently cluster system instances for large-scale Web services. |
Shenglin Zhang; Dongwen Li; Zhenyu Zhong; Jun Zhu; Minghan Liang; Jiexi Luo; Yongqian Sun; Ya Su; Sibo Xia; Zhongyou Hu; Yuzhi Zhang; Dan Pei; Jiyan Sun; Yinlong Liu; |
| 174 | A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Even though many anomaly detection techniques have been proposed, few of them can be directly applied to a given microservice or cloud server in industrial environment. To settle these challenges, this paper presents SLA-VAE, a semi-supervised learning based active anomaly detection framework using variational auto-encoder. |
Tao Huang; Pengfei Chen; Ruipeng Li; |
| 175 | DREW: Efficient Winograd CNN Inference with Deep Reuse Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a new inference method, called DREW, which combines deep reuse with Winograd for further accelerating CNNs. |
Ruofan Wu; Feng Zhang; Jiawei Guan; Zhen Zheng; Xiaoyong Du; Xipeng Shen; |
| 176 | PaSca: A Graph Neural Architecture Search System Under The Scalable Paradigm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes PaSca, a new paradigm and system that offers a principled approach to systemically construct and explore the design space for scalable GNNs, rather than studying individual designs. |
Wentao Zhang; Yu Shen; Zheyu Lin; Yang Li; Xiaosen Li; Wen Ouyang; Yangyu Tao; Zhi Yang; Bin Cui; |
| 177 | TRACE: A Fast Transformer-based General-Purpose Lossless Compressor Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a fast general-purpose lossless compressor, TRACE, by designing a compression-friendly structure based on a single-layer transformer. |
Yu Mao; Yufei Cui; Tei-Wei Kuo; Chun Jason Xue; |
| 178 | FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To solve the aforementioned challenges of multilingual federated NLU, we propose a plug-and-play knowledge composition (KC) module, called FedKC, which exchanges knowledge among clients without sharing raw data. |
Haoyu Wang; Handong Zhao; Yaqing Wang; Tong Yu; Jiuxiang Gu; Jing Gao; |
| 179 | Not All Layers Are Equal: A Layer-Wise Adaptive Approach Toward Large-Scale DNN Training Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To this phenomenon, we hypothesize that existing lr scaling methods overlook the subtle but important differences across “layers” in training, which results in the degradation of the overall model quality. From this hypothesis, we propose a novel approach (LENA) toward the learning rate scaling for large-scale DNN training, employing: (1) a layer-wise adaptive lr scaling to adjust lr for each layer individually, and (2) a layer-wise state-aware warm-up to track the state of the training for each layer and finish its warm-up automatically. |
Yunyong Ko; Dongwon Lee; Sang-Wook Kim; |
| 180 | Pyramid: Enabling Hierarchical Neural Networks with Edge Computing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework named Pyramid that unleashes the potential of edge AI by facilitating homogeneous and heterogeneous hierarchical ML inferences. |
Qiang He; Zeqian Dong; Feifei Chen; Shuiguang Deng; Weifa Liang; Yun Yang; |
| 181 | A Sampling-based Learning Framework for Big Databases Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite the promise, to obtain a decent policy model in the domain of database optimization is still challenging — primarily due to the inherent computational overhead involved in the data hungry RL frameworks — in particular on large databases. In the line of mitigating this adverse effect, we propose Mirror in this work. |
Jingtian Zhang; Sai Wu; Junbo Zhao; Zhongle Xie; Feifei Li; Yusong Gao; Gang Chen; |
| 182 | The Case of SPARQL UNION, FILTER and DISTINCT Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we use previously published optimisation techniques for BGP-OPTIONAL queries as primitives, and show how they can be used for SPARQL queries with any intermix of UNION, FILTER, and DISTINCT clauses. |
Medha Atre; |
| 183 | UniParser: A Unified Log Parser for Heterogeneous Log Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose UniParser to capture the common logging behaviours from heterogeneous log data. |
Yudong Liu; Xu Zhang; Shilin He; Hongyu Zhang; Liqun Li; Yu Kang; Yong Xu; Minghua Ma; Qingwei Lin; Yingnong Dang; Saravan Rajmohan; Dongmei Zhang; |
| 184 | Element-guided Temporal Graph Representation Learning for Temporal Sets Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel element-guided temporal graph neural network to tackle the above issue in temporal sets prediction. |
Le Yu; Guanghui Wu; Leilei Sun; Bowen Du; Weifeng Lv; |
| 185 | Hypercomplex Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To bridge these gaps, in this paper, we propose a novel recommendation framework named HyperComplex Graph Collaborative Filtering (HCGCF). |
Anchen Li; Bo Yang; Huan Huo; Farookh Hussain; |
| 186 | Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Guided by these findings, it is meaningful to design a deeper hybrid SR model to ensemble the capacity of both self-attentive and convolutional architectures for SR tasks. In this work, we aim to achieve this goal in the automatic algorithm sense, and propose NASR, an efficient neural architecture search (NAS) method that can automatically select the architecture operation on each layer. |
Mingyue Cheng; Zhiding Liu; Qi Liu; Shenyang Ge; Enhong Chen; |
| 187 | Outlier Detection for Streaming Task Assignment in Crowdsourcing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a framework called Outlier Detection for Streaming Task Assignment that aims to improve robustness by detecting malicious actors. |
Yan Zhao; Xuanhao Chen; Liwei Deng; Tung Kieu; Chenjuan Guo; Bin Yang; Kai Zheng; Christian S. Jensen; |
| 188 | Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing methods usually exploit the uniform or popularity sampler as proposal distributions, leading to a large bias of gradient estimation. To this end, we propose to decompose the inner-product-based softmax probability based on the inverted multi-index, leading to sublinear-time and highly accurate sampling. |
Jin Chen; Defu Lian; Binbin Jin; Xu Huang; Kai Zheng; Enhong Chen; |
| 189 | Graph Neural Transport Networks with Non-local Attentions for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we present Graph Optimal Transport Networks (GOTNet) to capture long-range dependencies without increasing the depths of GNNs. |
Huiyuan Chen; Chin-Chia Michael Yeh; Fei Wang; Hao Yang; |
| 190 | Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes a novel OCCF framework, named as ConCF, that exploits the complementarity from heterogeneous objectives throughout the training process, generating a more generalizable model. |
Seongku Kang; Dongha Lee; Wonbin Kweon; Junyoung Hwang; Hwanjo Yu; |
| 191 | AutoField: Automating Feature Selection in Deep Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose an AutoML framework that can adaptively select the essential feature fields in an automatic manner. |
Yejing Wang; Xiangyu Zhao; Tong Xu; Xian Wu; |
| 192 | OffDQ: An Offline Deep Learning Framework for QoS Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we systematically analyze the various challenges associated with the QoS prediction algorithm and propose solution strategies to overcome the challenges, and thereby propose a novel offline framework using deep neural architectures for QoS prediction to achieve our goals. |
Soumi Chattopadhyay; Richik Chanda; Suraj Kumar; Chandranath Adak; |
| 193 | Learning Recommenders for Implicit Feedback with Importance Resampling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose an Adaptive Sampling method based on Importance Resampling (AdaSIR for short), which is not only almost equally efficient and accurate for any recommender models, but also can robustly accommodate arbitrary proposal distributions. |
Jin Chen; Defu Lian; Binbin Jin; Kai Zheng; Enhong Chen; |
| 194 | Similarity-based Multi-Domain Dialogue State Tracking with Copy Mechanisms for Task-based Virtual Personal Assistants Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new Similarity-based Multi-domain Dialogue State Tracking model (SM-DST) that uses retrieval-inspired and fine-grained contextual token-level similarity approaches to efficiently and effectively track dialogue state. |
Jarana Manotumruksa; Jeffrey Dalton; Edgar Meij; Emine Yilmaz; |
| 195 | Learning Robust Recommenders Through Cross-Model Agreement Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a general framework to learn robust recommenders from implicit feedback. |
Yu Wang; Xin Xin; Zaiqiao Meng; Joemon M Jose; Fuli Feng; Xiangnan He; |
| 196 | Lessons from The AdKDD’21 Privacy-Preserving ML Challenge Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD’21, a premier workshop on Advertising Science with data provided by advertising company Criteo. In this paper, we describe the challenge tasks, the structure of the available datasets, report the challenge results, and enable its full reproducibility. |
Eustache Diemert; Romain Fabre; Alexandre Gilotte; Fei Jia; Basile Leparmentier; Jeremie Mary; Zhonghua Qu; Ugo Tanielian; Hui Yang; |
| 197 | Sequential Recommendation Via Stochastic Self-Attention Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel STOchastic Self-Attention (STOSA) to overcome these issues. STOSA, in particular, embeds each item as a stochastic Gaussian distribution, the covariance of which encodes the uncertainty. |
Ziwei Fan; Zhiwei Liu; Yu Wang; Alice Wang; Zahra Nazari; Lei Zheng; Hao Peng; Philip S. Yu; |
| 198 | Rating Distribution Calibration for Selection Bias Mitigation in Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing approaches to mitigate selection bias, such as data imputation and inverse propensity score, are sensitive to the quality of the additional imputation or propensity estimation models. To break these limitations, in this work, we propose a novel self-supervised learning (SSL) framework, i.e., Rating Distribution Calibration (RDC), to tackle selection bias without introducing additional models. |
Haochen Liu; Da Tang; Ji Yang; Xiangyu Zhao; Hui Liu; Jiliang Tang; Youlong Cheng; |
| 199 | Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Unfortunately, existing works mainly focus on tackling the learning of common knowledge across modalities, while the specific characteristics of each modality is discarded, which may inevitably degrade the recommendation performance. To this end, we propose a pretraining framework PAMD, which stands for PretrAining Modality-Disentangled Representations Model. |
Tengyue Han; Pengfei Wang; Shaozhang Niu; Chenliang Li; |
| 200 | MINDSim: User Simulator for News Recommenders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we build a user simulator, namely MINDSim, for news recommendation. |
Xufang Luo; Zheng Liu; Shitao Xiao; Xing Xie; Dongsheng Li; |
| 201 | UKD: Debiasing Conversion Rate Estimation Via Uncertainty-regularized Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes an uncertainty-regularized knowledge distillation (UKD) framework to debias CVR estimation via distilling knowledge from unclicked ads. |
Zixuan Xu; Penghui Wei; Weimin Zhang; Shaoguo Liu; Liang Wang; Bo Zheng; |
| 202 | FeedRec: News Feed Recommendation with Various User Feedbacks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a news feed recommendation method that can exploit various kinds of user feedbacks to enhance both user interest modeling and model training. |
Chuhan Wu; Fangzhao Wu; Tao Qi; Qi Liu; Xuan Tian; Jie Li; Wei He; Yongfeng Huang; Xing Xie; |
| 203 | Multi-level Recommendation Reasoning Over Knowledge Graphs with Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a reinforcement learning framework for multi-level recommendation reasoning over KGs, which leverages both ontology-view and instance-view KGs to model multi-level user interests. |
Xiting Wang; Kunpeng Liu; Dongjie Wang; Le Wu; Yanjie Fu; Xing Xie; |
| 204 | Dynamic Gaussian Embedding of Authors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Tasks such as author classification, author identification or link prediction are difficult to solve in such complex data settings. We propose a new representation learning model, DGEA (for Dynamic Gaussian Embedding of Authors), that is more suited to solve these tasks by capturing this temporal evolution. |
Antoine Gourru; Julien Velcin; Christophe Gravier; Julien Jacques; |
| 205 | GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel idea to learn the graph structure among users and make recommendations collectively in a coupled framework. |
Chunyu Wei; Bing Bai; Kun Bai; Fei Wang; |
| 206 | Towards A Multi-View Attentive Matching for Personalized Expert Finding Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a personalized expert finding method with a multi-view attentive matching mechanism. |
Qiyao Peng; Hongtao Liu; Yinghui Wang; Hongyan Xu; Pengfei Jiao; Minglai Shao; Wenjun Wang; |
| 207 | Deep Unified Representation for Heterogeneous Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: And it is indeed quite challenging to represent items with different feature spaces jointly. To tackle this problem, we propose a kernel-based neural network, namely deep unified representation (or DURation) for heterogeneous recommendation, to jointly model unified representations of heterogeneous items while preserving their original feature space topology structures. |
Chengqiang Lu; Mingyang Yin; Shuheng Shen; Luo Ji; Qi Liu; Hongxia Yang; |
| 208 | Multiple Choice Questions Based Multi-Interest Policy Learning for Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To effectively cope with the new CRS learning setting, in this paper, we propose a novel learning framework, namely Multiple Choice questions based Multi-Interest Policy Learning (MCMIPL). |
Yiming Zhang; Lingfei Wu; Qi Shen; Yitong Pang; Zhihua Wei; Fangli Xu; Bo Long; Jian Pei; |
| 209 | Graph-based Extractive Explainer for Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes and sentences for extraction-based explanation. |
Peng Wang; Renqin Cai; Hongning Wang; |
| 210 | Intent Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To investigate the benefits of latent intents and leverage them effectively for recommendation, we propose Intent Contrastive Learning (ICL), a general learning paradigm that leverages a latent intent variable into SR. |
Yongjun Chen; Zhiwei Liu; Jia Li; Julian McAuley; Caiming Xiong; |
| 211 | Learning to Augment for Casual User Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To bridge the gap, we propose a model-agnostic framework L2Aug to improve recommendations for casual users through data augmentation, without sacrificing core user experience. |
Jianling Wang; Ya Le; Bo Chang; Yuyan Wang; Ed H. Chi; Minmin Chen; |
| 212 | Unbiased Sequential Recommendation with Latent Confounders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the observational data may have been contaminated by the exposure or selection biases, which renders the learned sequential models unreliable. In order to solve this fundamental problem, in this paper, we propose to reformulate the sequential recommendation task with the potential outcome framework, where we are able to clearly understand the data bias mechanism and correct it by re-weighting the training instances with the inverse propensity score (IPS). |
Zhenlei Wang; Shiqi Shen; Zhipeng Wang; Bo Chen; Xu Chen; Ji-Rong Wen; |
| 213 | MetaBalance: Improving Multi-Task Recommendations Via Adapting Gradient Magnitudes of Auxiliary Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a new method: MetaBalance to balance auxiliary losses via directly manipulating their gradients w.r.t the shared parameters in the multi-task network. |
Yun He; Xue Feng; Cheng Cheng; Geng Ji; Yunsong Guo; James Caverlee; |
| 214 | Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, without explicit regularization, the interest embeddings may not be distinct from each other nor semantically reflect representative historical items. Towards this end, we propose the Re4 framework, which leverages the backward flow to reexamine each interest embedding. |
Shengyu Zhang; Lingxiao Yang; Dong Yao; Yujie Lu; Fuli Feng; Zhou Zhao; Tat-seng Chua; Fei Wu; |
| 215 | Generative Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Previous models mostly focus on designing different models to fit the observed data, which can be quite sparse in real-world scenarios. To alleviate this problem, in this paper, we propose a novel generative session-based recommendation framework. |
Zhidan Wang; Wenwen Ye; Xu Chen; Wenqiang Zhang; Zhenlei Wang; Lixin Zou; Weidong Liu; |
| 216 | MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a feature-aware binning framework, called Multiple Boosting Calibration Trees (MBCT), along with a multi-view calibration loss to tackle the above issues. |
Siguang Huang; Yunli Wang; Lili Mou; Huayue Zhang; Han Zhu; Chuan Yu; Bo Zheng; |
| 217 | Adaptive Experimentation with Delayed Binary Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper presents an adaptive experimentation solution tailored for delayed binary feedback objectives by estimating the real underlying objectives before they materialize and dynamically allocating variants based on the estimates. |
Zenan Wang; Carlos Carrion; Xiliang Lin; Fuhua Ji; Yongjun Bao; Weipeng Yan; |
| 218 | Disentangling Long and Short-Term Interests for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, to address it, we propose a Contrastive learning framework to disentangle Long and Short-term interests for Recommendation (CLSR) with self-supervision. |
Yu Zheng; Chen Gao; Jianxin Chang; Yanan Niu; Yang Song; Depeng Jin; Yong Li; |
| 219 | CBR: Context Bias Aware Recommendation for Debiasing User Modeling and Click Prediction✱ Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To that end, in this paper, we design a novel Context Bias aware Recommendation (CBR) model for describing and debiasing the context bias caused by comprehensive interactions between multiple items. |
Zhi Zheng; Zhaopeng Qiu; Tong Xu; Xian Wu; Xiangyu Zhao; Enhong Chen; Hui Xiong; |
| 220 | Meta-Learning Helps Personalized Product Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Under the records arriving sequentially setting, we propose an online variational inference algorithm to update meta-knowledge over time. |
Bin Wu; Zaiqiao Meng; Qiang Zhang; Shangsong Liang; |
| 221 | Sequential Recommendation with Decomposed Item Feature Routing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, existing sequential models usually represent each item by a unified embedding, which fails to distinguish item features, let along modeling the feature sequences. To bridge this gap, in this paper, we propose a novel sequential recommender model by learning the key item feature sequences underlying user behaviors, which facilitates more focused model optimization and better recommendation performance. |
Kun Lin; Zhenlei Wang; Shiqi Shen; Zhipeng Wang; Bo Chen; Xu Chen; |
| 222 | An Empirical Investigation of Personalization Factors on TikTok Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok, follow- and like-feature, as well as how the recommended content changes as a user watches certain posts longer than others. |
Maximilian Boeker; Aleksandra Urman; |
| 223 | LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Thus, how to select the right amount of incentives (i.e. treatment) to each user under budget constraints is an important research problem with great practical implications. In this paper, we call such problem as a budget-constrained treatment selection (BTS) problem. |
Meng Ai; Biao Li; Heyang Gong; Qingwei Yu; Shengjie Xue; Yuan Zhang; Yunzhou Zhang; Peng Jiang; |
| 224 | Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the above issue, we propose a novel contrastive learning approach, named Neighborhood-enriched Contrastive Learning, named NCL, which explicitly incorporates the potential neighbors into contrastive pairs. |
Zihan Lin; Changxin Tian; Yupeng Hou; Wayne Xin Zhao; |
| 225 | Contrastive Learning for Knowledge Tracing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces a contrastive learning framework for knowledge tracing that reveals semantically similar or dissimilar examples of a learning history and stimulates to learn their relationships. |
Wonsung Lee; Jaeyoon Chun; Youngmin Lee; Kyoungsoo Park; Sungrae Park; |
| 226 | MCL: Mixed-Centric Loss for Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This can ignore valuable global information from other users and items, and lead to sub-optimal results. Motivated by this problem, we propose a novel loss which first leverages mining to select the most informative pairs, followed by a weighing process to allocate more weight to harder examples. |
Zhaolin Gao; Zhaoyue Cheng; Felipe Perez; Jianing Sun; Maksims Volkovs; |
| 227 | Off-policy Learning Over Heterogeneous Information for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we are the first to propose a novel off-policy learning augmented by meta-paths for the recommendation. |
Xiangmeng Wang; Qian Li; Dianer Yu; Guandong Xu; |
| 228 | FIRE: Fast Incremental Recommendation with Graph Signal Processing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these methods are faced with two key challenges: 1) model training and/or updating are time-consuming and 2) new users/items cannot be effectively handled. To this end, we propose the fast incremental recommendation (FIRE) method from a graph signal processing perspective. |
Jiafeng Xia; Dongsheng Li; Hansu Gu; Jiahao Liu; Tun Lu; Ning Gu; |
| 229 | Cross Pairwise Ranking for Unbiased Item Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism. |
Qi Wan; Xiangnan He; Xiang Wang; Jiancan Wu; Wei Guo; Ruiming Tang; |
| 230 | Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Aiming to address the above two problems simultaneously, we propose a mutually-regularized dual collaborative variational auto-encoder (MD-CVAE) for recommendation. |
Yaochen Zhu; Zhenzhong Chen; |
| 231 | Filter-enhanced MLP Is All You Need for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by it, we propose FMLP-Rec, an all-MLP model with learnable filters for sequential recommendation task. |
Kun Zhou; Hui Yu; Wayne Xin Zhao; Ji-Rong Wen; |
| 232 | QLUE: A Computer Vision Tool for Uniform Qualitative Evaluation of Web Pages Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we demonstrate the lack of qualitative evaluation metrics, and propose QLUE (QuaLitative Uniform Evaluation), a tool that automates the qualitative evaluation of web pages generated by web complexity solutions with respect to their original versions using computer vision. |
Waleed Hashmi; Moumena Chaqfeh; Lakshminarayanan Subramanian; Yasir Zaki; |
| 233 | Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: One challenge in using such feedback is inferring a user’s semantic intent from the open-ended terms used to describe an item, and using it to refine recommendation results. Leveraging concept activation vectors (CAVs) [21], we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in RSs. |
Christina Göpfert; Yinlam Chow; Chih-Wei Hsu; Ivan Vendrov; Tyler Lu; Deepak Ramachandran; Craig Boutilier; |
| 234 | Distributional Contrastive Embedding for Clarification-based Conversational Critiquing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Correcting such errors requires reasoning about the level of generality and specificity of preferences and verifying that the user has expressed the correct level of generality. To this end, we propose a novel clarification-based conversational critiquing framework that allows the system to clarify user preferences as it accepts critiques. |
Tianshu Shen; Zheda Mai; Ga Wu; Scott Sanner; |
| 235 | Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we use podcast recommendations with two engagement signals: Subscription vs. Plays to show that the choice of user engagement matters. |
Zahra Nazari; Praveen Chandar; Ghazal Fazelnia; Catherine M. Edwards; Benjamin Carterette; Mounia Lalmas; |
| 236 | Efficient Online Learning to Rank for Sequential Music Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Nevertheless, these approaches can suffer from slow convergence as a result of their random exploration component and get stuck in local minima as a result of their session-agnostic exploitation component. To overcome these limitations, we propose a novel online learning to rank approach which efficiently explores the space of candidate recommendation models by restricting itself to the orthogonal complement of the subspace of previous underperforming exploration directions. |
Pedro Dalla Vecchia Chaves; Bruno L. Pereira; Rodrygo L. T. Santos; |
| 237 | Learn Over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework named Search-based Time-Aware Recommendation (STARec), which captures the evolving demands of users over time through a unified search-based time-aware model. |
Jiarui Jin; Xianyu Chen; Weinan Zhang; Junjie Huang; Ziming Feng; Yong Yu; |
| 238 | HRCF: Enhancing Collaborative Filtering Via Hyperbolic Geometric Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Regarding the geometric properties of hyperbolic space, we bring up a Hyperbolic Regularization powered Collaborative Filtering (HRCF) and design a geometric-aware hyperbolic regularizer. |
Menglin Yang; Min Zhou; Jiahong Liu; Defu Lian; Irwin King; |
| 239 | Prototype Feature Extraction for Multi-task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, MTL is sensitive to relationships among tasks and may have performance degradation in real-world applications, because existing neural-based MTL models often share the same network structures and original input features. To address this issue, we propose a novel multi-task learning model based on Prototype Feature Extraction (PFE) to balance task-specific objectives and inter-task relationships. |
Shen Xin; Yuhang Jiao; Cheng Long; Yuguang Wang; Xiaowei Wang; Sen Yang; Ji Liu; Jie Zhang; |
| 240 | Stochastic-Expert Variational Autoencoder for Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Motivated by the recent successes of deep generative models used for collaborative filtering, we propose a novel framework of VAE for collaborative filtering using multiple experts and stochastic expert selection, which allows the model to learn a richer and more complex latent representation of user preferences. |
Yoon-Sik Cho; Min-hwan Oh; |
| 241 | Left or Right: A Peek Into The Political Biases in Email Spam Filtering Algorithms During US Election 2020 Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. |
Hassan Iqbal; Usman Mahmood Khan; Hassan Ali Khan; Muhammad Shahzad; |
| 242 | Evidence-aware Fake News Detection with Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). |
Weizhi Xu; Junfei Wu; Qiang Liu; Shu Wu; Liang Wang; |
| 243 | BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the challenge, we propose a non-RL Bipartite Scalable framework for Online Disease diAgnosis, called BSODA. |
Weijie He; Xiaohao Mao; Chao Ma; Yu Huang; José Miguel Hernàndez-Lobato; Ting Chen; |
| 244 | Leveraging Google’s Publisher-Specific IDs to Detect Website Administration Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel, graph-based methodology to detect administration of websites on the Web, by exploiting the ad-related publisher-specific IDs. |
Emmanouil Papadogiannakis; Panagiotis Papadopoulos; Evangelos P. Markatos; Nicolas Kourtellis; |
| 245 | Second-level Digital Divide: A Longitudinal Study of Mobile Traffic Consumption Imbalance in France Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the interaction between the consumption of digital services via mobile devices and urbanization levels, using measurement data collected in an operational network serving the whole territory of France. |
Sachit Mishra; Zbigniew Smoreda; Marco Fiore; |
| 246 | The Impact of Twitter Labels on Misinformation Spread and User Engagement: Lessons from Trump’s Election Tweets Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This study investigates the warning labels that Twitter placed on Donald Trump’s false tweets about the 2020 US Presidential election. |
Orestis Papakyriakopoulos; Ellen Goodmann; |
| 247 | PopNet: Real-Time Population-Level Disease Prediction with Data Latency Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a real-time population-level disease prediction model which captures data latency (PopNet) and incorporates the updated data for improved predictions. |
Junyi Gao; Cao Xiao; Lucas M. Glass; Jimeng Sun; |
| 248 | Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: With these, we propose a hierarchical attention method optimized to factor in the increasing depression severity levels through a soft probability distribution. |
Usman Naseem; Adam G. Dunn; Jinman Kim; Matloob Khushi; |
| 249 | Identification of Disease or Symptom Terms in Reddit to Improve Health Mention Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To advance the HMC research, we present a Reddit health mention dataset (RHMD), a new dataset of multi-domain Reddit data for the HMC. |
Usman Naseem; Jinman Kim; Matloob Khushi; Adam G. Dunn; |
| 250 | Jettisoning Junk Messaging in The Era of End-to-End Encryption: A Case Study of WhatsApp Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The sending of unwanted junk messages by unknown contacts via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. We address this gap by studying junk messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp groups in India. |
Pushkal Agarwal; Aravindh Raman; Damiola Ibosiola; Nishanth Sastry; Gareth Tyson; Kiran Garimella; |
| 251 | Understanding Conflicts in Online Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The current research aims to understand how online conflicts arise in online personal conversations. |
Sharon Levy; Robert E. Kraut; Jane A. Yu; Kristen M. Altenburger; Yi-Chia Wang; |
| 252 | Emotion Bubbles: Emotional Composition of Online Discourse Before and After The COVID-19 Outbreak Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We traced how the overall emotional states of individual Twitter users changed before and after the pandemic. |
Assem Zhunis; Gabriel Lima; Hyeonho Song; Jiyoung Han; Meeyoung Cha; |
| 253 | Characterizing, Detecting, and Predicting Online Ban Evasion Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we conduct the first data-driven study of ban evasion, i.e., the act of circumventing bans on an online platform, leading to temporally disjoint operation of accounts by the same user. |
Manoj Niverthi; Gaurav Verma; Srijan Kumar; |
| 254 | Controlled Analyses of Social Biases in Wikipedia Bios Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a methodology for analyzing Wikipedia pages about people that isolates dimensions of interest (e.g., gender), from other attributes (e.g., occupation). |
Anjalie Field; Chan Young Park; Kevin Z. Lin; Yulia Tsvetkov; |
| 255 | EvidenceNet: Evidence Fusion Network for Fact Verification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To tackle the problems, we propose an evidence fusion network called EvidenceNet. |
Zhendong Chen; Siu Cheung Hui; Fuzhen Zhuang; Lejian Liao; Fei Li; Meihuizi Jia; Jiaqi Li; |
| 256 | Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As the efficiency and fairness objectives can be in conflict with each other, we propose a joint optimization framework that allows conference organizers to design schedules that balance (i.e., allow trade-offs) among efficiency, participant fairness and speaker fairness objectives. |
Gourab K. Patro; Prithwish Jana; Abhijnan Chakraborty; Krishna P. Gummadi; Niloy Ganguly; |
| 257 | Context-based Collective Preference Aggregation for Prioritizing Crowd Opinions in Social Decision-making Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In our scenario, additional contextual information, such as the text contents of the opinions, can potentially promote the aggregation performance. Therefore, we propose preference aggregation approaches that can effectively incorporate contextual information by externally or internally building the relations between the opinion contexts and preference scores. |
Jiyi Li; |
| 258 | How Misinformation Density Affects Health Information Search Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We suspect that the percentage of misinformation search results (misinformation density) may influence people’s search activities, learning outcomes, and search experience. We conducted a zoom-mediated “lab” user study to examine this matter. |
Qiurong Song; Jiepu Jiang; |
| 259 | Assessing The Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in The US Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. |
Jing Ma; Yushun Dong; Zheng Huang; Daniel Mietchen; Jundong Li; |
| 260 | What Does Perception Bias on Social Networks Tell Us About Friend Count Satisfaction? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Less studied is user sentiment about their networks, such as a user’s satisfaction with their number of friends. We surveyed over 85,000 Facebook users about how satisfied they were with their number of friends on Facebook, connecting these responses to their on-platform activity. |
Shen Yan; Kristen M. Altenburger; Yi-Chia Wang; Justin Cheng; |
| 261 | How Do Mothers and Fathers Talk About Parenting to Different Audiences?: Stereotypes and Audience Effects: An Analysis of R/Daddit, R/Mommit, and R/Parenting Using Topic Modelling Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The present study analyses discussions about parenting on Reddit (i.e., a content aggregation website) to explore audience effects and gender stereotypes. |
Melody Sepahpour-Fard; Michael Quayle; |
| 262 | Conspiracy Brokers: Understanding The Monetization of YouTube Conspiracy Theories Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Previous work has established YouTube as one of the most popular sites for people to host and discuss different theories. In this paper, we present an analysis of monetization methods of conspiracy theorist YouTube creators and the types of advertisers potentially targeting this content. |
Cameron Ballard; Ian Goldstein; Pulak Mehta; Genesis Smothers; Kejsi Take; Victoria Zhong; Rachel Greenstadt; Tobias Lauinger; Damon McCoy; |
| 263 | Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study the problem of mitigating radicalization pathways using a graph-based approach. |
Francesco Fabbri; Yanhao Wang; Francesco Bonchi; Carlos Castillo; Michael Mathioudakis; |
| 264 | GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walk techniques to capture the wider context of a discussion thread in a principled fashion. |
Vibhor Agarwal; Sagar Joglekar; Anthony P. Young; Nishanth Sastry; |
| 265 | Zero-Shot Stance Detection Via Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: As such, it is important to distinguish these two types of stance features when learning stance features of unseen targets. To this end, in this paper, we revisit ZSSD from a novel perspective by developing an effective approach to distinguish the types (target-invariant/-specific) of stance features, so as to better learn transferable stance features. |
Bin Liang; Zixiao Chen; Lin Gui; Yulan He; Min Yang; Ruifeng Xu; |
| 266 | Is Least-Squares Inaccurate in Fitting Power-Law Distributions? The Criticism Is Complete Nonsense Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In the last two decades, however, some researchers criticized that least-squares was substantially inaccurate in fitting power-law distributions; such criticism has caused a strong bias in research community. In this paper, we conduct extensive experiments to rebut that such criticism is complete nonsense. |
Xiaoshi Zhong; Muyin Wang; Hongkun Zhang; |
| 267 | Seesaw Counting Filter: An Efficient Guardian for Vulnerable Negative Keys During Dynamic Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the problem, we propose SeeSaw Counting Filter (SSCF), which is innovated with encapsulating the vulnerable negative keys into a unified counter array named seesaw counter array, and dynamically modulating (or varying) the applied hash functions to guard the encapsulated keys from being misidentified. |
Meng Li; Deyi Chen; Haipeng Dai; Rongbiao Xie; Siqiang Luo; Rong Gu; Tong Yang; Guihai Chen; |
| 268 | Recommendation Unlearning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose RecEraser, a general and efficient machine unlearning framework tailored to recommendation tasks. |
Chong Chen; Fei Sun; Min Zhang; Bolin Ding; |
| 269 | KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Furthermore, there exists abundant semantic and prior knowledge among the relation labels that cannot be ignored. To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). |
Xiang Chen; Ningyu Zhang; Xin Xie; Shumin Deng; Yunzhi Yao; Chuanqi Tan; Fei Huang; Luo Si; Huajun Chen; |
| 270 | Rumor Detection on Social Media with Graph Adversarial Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, these conventional models may lead to poor generalization, and lack robustness in the face of noise and adversarial rumors, or even the conversational structures that is deliberately perturbed (e.g., adding or deleting some comments). In this paper, we propose a novel Graph Adversarial Contrastive Learning (GACL) method to fight these complex cases, where the contrastive learning is introduced as part of the loss function for explicitly perceiving differences between conversational threads of the same class and different classes. |
Tiening Sun; Zhong Qian; Sujun Dong; Peifeng Li; Qiaoming Zhu; |
| 271 | Detecting False Rumors from Retweet Dynamics on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process. |
Christof Naumzik; Stefan Feuerriegel; |
| 272 | VisGNN: Personalized Visualization Recommendationvia Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop a Graph Neural Network (GNN) framework for the problem of personalized visualization recommendation. |
Fayokemi Ojo; Ryan A. Rossi; Jane Hoffswell; Shunan Guo; Fan Du; Sungchul Kim; Chang Xiao; Eunyee Koh; |
| 273 | TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. |
Dongha Lee; Jiaming Shen; Seongku Kang; Susik Yoon; Jiawei Han; Hwanjo Yu; |
| 274 | Revisiting Graph Based Social Recommendation: A Distillation Enhanced Social Graph Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We design metrics to evaluate the social information, which can provide guidance about whether and how we should use this information in the RS task. Based on these analyses, we propose a Distillation Enhanced SocIal Graph Network (DESIGN). |
Ye Tao; Ying Li; Su Zhang; Zhirong Hou; Zhonghai Wu; |
| 275 | Genre-Controllable Story Generation Via Supervised Contrastive Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While controllable text generation has received attention due to the recent advances in large-scale pre-trained language models, there is a lack of research that focuses on story-specific controllability. To address this, we present Story Control via Supervised Contrastive learning model (SCSC), to create a story conditioned on genre. |
JinUk Cho; MinSu Jeong; JinYeong Bak; Yun-Gyung Cheong; |
| 276 | Is This Question Real? Dataset Collection on Perceived Intentions and Implicit Attack Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This study focuses on identifying the implicit attacks and negative intentions in text-based conversation from the reader’s point of view. |
Maryam Sadat Mirzaei; Kourosh Meshgi; Satoshi Sekine; |
| 277 | Modeling Inter Round Attack of Online Debaters for Winner Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on assessing the overall performance of debaters in a multi-round debate on online forums. |
Fa-Hsuan Hsiao; An-Zi Yen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 278 | A Guided Topic-Noise Model for Short Texts Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: These domain experts often turn to topic models to help them see the entire landscape of the conversation, but unsupervised topic models often produce topic sets that miss topics experts expect or want to see. To solve this problem, we propose Guided Topic-Noise Model (GTM), a semi-supervised topic model designed with large domain-specific social media data sets in mind. |
Robert Churchill; Lisa Singh; Rebecca Ryan; Pamela Davis-Kean; |
| 279 | Knowledge-based Temporal Fusion Network for Interpretable Online Video Popularity Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Knowledge-based Temporal Fusion Network (KTFN) that incorporates knowledge graph representation to address the aforementioned challenges in the task of online video popularity prediction. |
Shisong Tang; Qing Li; Xiaoteng Ma; Ci Gao; Dingmin Wang; Yong Jiang; Qian Ma; Aoyang Zhang; Hechang Chen; |
| 280 | “I Have No Text in My Post”: Using Visual Hints to Model User Emotions in Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, prior research has not sufficiently considered trends of social media use—the increasing use of images and the decreasing use of text—nor identified the features of images in social media that are likely to be different from those in non-social media. Our study aims to fill this gap by (1) considering the notion of visual hints that depict contextual information of images, (2) presenting their characteristics in positive or negative emotions, and (3) demonstrating their effectiveness in emotion prediction modeling through an in-depth analysis of their relationship with the text in the same posts. |
Junho Song; Kyungsik Han; Sang-Wook Kim; |
| 281 | Cross-modal Ambiguity Learning for Multimodal Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A fundamental challenge of multimodal fake news detection lies in the inherent ambiguity across different content modalities, i.e., decisions made from unimodalities may disagree with each other, which may lead to inferior multimodal fake news detection. To address this issue, we formulate the cross-modal ambiguity learning problem from an information-theoretic perspective and propose CAFE — an ambiguity-aware multimodal fake news detection method. |
Yixuan Chen; Dongsheng Li; Peng Zhang; Jie Sui; Qin Lv; Lu Tun; Li Shang; |
| 282 | Contrastive Learning with Positive-Negative Frame Mask for Music Representation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, those coarse-grained methods neglect some inessential or noisy elements at the frame level, which may be detrimental to the model to learn the effective representation of music. Towards this end, this paper proposes a novel Positive-nEgative frame mask for Music Representation based on the contrastive learning framework, abbreviated as PEMR. |
Dong Yao; Zhou Zhao; Shengyu Zhang; Jieming Zhu; Yudong Zhu; Rui Zhang; Xiuqiang He; |
| 283 | CycleNER: An Unsupervised Training Approach for Named Entity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose CycleNER, an unsupervised approach based on cycle-consistency training that uses two functions: (i) sentence-to-entity – S2E and (ii) entity-to-sentence – E2S, to carry out the NER task. |
Andrea Iovine; Anjie Fang; Besnik Fetahu; Oleg Rokhlenko; Shervin Malmasi; |
| 284 | A Meta-learning Based Stress Category Detection Framework on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we build a meta-learning based stress category detection framework, which can learn how to distinguish a new stress category with very little data through learning on frequently appeared categories without relying on any lexicon. |
Xin Wang; Lei Cao; Huijun Zhang; Ling Feng; Yang Ding; Ningyun Li; |
| 285 | Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Through extensive experiments on data from a large-scale music streaming platform, we present insights on the trade-offs that exist across different objectives, and demonstrate that the proposed framework leads to a superior, just-in-time balancing across the various metrics of interest. |
Emanuele Bugliarello; Rishabh Mehrotra; James Kirk; Mounia Lalmas; |
| 286 | Massive Text Normalization Via An Efficient Randomized Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present Flan (Fast LSH Algorithm for Text Normalization), a scalable randomized algorithm to clean and canonicalize massive text data. |
Nan Jiang; Chen Luo; Vihan Lakshman; Yesh Dattatreya; Yexiang Xue; |
| 287 | Effective Messaging on Social Media: What Makes Online Content Go Viral? Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose and test three content-based hypotheses that significantly increase message virality. |
Maryam Mousavi; Hasan Davulcu; Mohsen Ahmadi; Robert Axelrod; Richard Davis; Scott Atran; |
| 288 | Using Web Data to Reveal 22-Year History of Sneaker Designs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Based on data encompassing 22 years from 1999 to 2020, we propose a sneaker design index that helps track changes in the design characteristics of sneakers using a contrastive learning method. |
Sungkyu Park; Hyeonho Song; Sungwon Han; Berhane Weldegebriel; Lev Manovich; Emanuele Arielli; Meeyoung Cha; |
| 289 | AmpSum: Adaptive Multiple-Product Summarization Towards Improving Recommendation Captions Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose an Adaptive Multiple-Product Summarization framework (AmpSum) that automatically and adaptively generates widget captions based on different recommended products. |
Quoc-Tuan Truong; Tong Zhao; Changhe Yuan; Jin Li; Jim Chan; Soo-Min Pantel; Hady W. Lauw; |
| 290 | DUET: A Generic Framework for Finding Special Quadratic Elements in Data Streams Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While data streams nowadays are usually high-dimensional, most prior works focus on special items according to a certain primary dimension and yield little insight into the correlations between dimensions. Therefore, we propose to find special quadratic elements to reveal close correlations. |
Jiaqian Liu; Haipeng Dai; Rui Xia; Meng Li; Ran Ben Basat; Rui Li; Guihai Chen; |
| 291 | User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction, by jointly learning User Satisfaction Estimation and Dialogue Act Recognition tasks. |
Yang Deng; Wenxuan Zhang; Wai Lam; Hong Cheng; Helen Meng; |
| 292 | Can Small Heads Help? Understanding and Improving Multi-Task Generalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we offer insights on the under-explored trade-off between minimizing task training conflicts in multi-task learning and improving multi-task generalization, i.e. the generalization capability of the shared presentation across all tasks. |
Yuyan Wang; Zhe Zhao; Bo Dai; Christopher Fifty; Dong Lin; Lichan Hong; Li Wei; Ed H. Chi; |
| 293 | Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To pursue a better balance between performance and efficiency, we propose the first quantized representation learning method for cross-view video retrieval, namely Hybrid Contrastive Quantization (HCQ). |
Jinpeng Wang; Bin Chen; Dongliang Liao; Ziyun Zeng; Gongfu Li; Shu-Tao Xia; Jin Xu; |
| 294 | Explainable Neural Rule Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In order to improve the explainability of neural networks, we propose a novel method—Explainable Neural Rule Learning (denoted as ENRL), with the aim to integrate the expressiveness of neural networks and the explainability of rule-based systems. |
Shaoyun Shi; Yuexiang Xie; Zhen Wang; Bolin Ding; Yaliang Li; Min Zhang; |
| 295 | Making Decision Like Human: Joint Aspect Category Sentiment Analysis and Rating Prediction with Fine-to-Coarse Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we gain the inspiration from human intuition, presenting an innovative from-fine-to-coarse reasoning framework for better joint task performance. |
Hao Fei; Jingye Li; Yafeng Ren; Meishan Zhang; Donghong Ji; |
| 296 | Significance and Coverage in Group Testing on The Social Web Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In particular, we address the challenges that arise in the context of testing an input hypothesis on many data samples, in our case, user groups. |
Nassim Bouarour; Idir Benouaret; Sihem Amer-Yahia; |
| 297 | Geospatial Entity Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In order to achieve automatic linking of geospatial data, a unified representation of entities with heterogeneous attributes and their geographical context, is needed. To this end, we propose Geo-ER1, a joint framework that combines Transformer-based language models, that have been successfully applied in ER, with a novel learning-based architecture to represent the geospatial character of the entity. |
Pasquale Balsebre; Dezhong Yao; Gao Cong; Zhen Hai; |
| 298 | A Deep Markov Model for Clickstream Analytics in Online Shopping Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we develop a novel, theory-informed machine learning model to account for different shopping phases as defined in marketing theory. |
Yilmazcan Ozyurt; Tobias Hatt; Ce Zhang; Stefan Feuerriegel; |
| 299 | DiriE: Knowledge Graph Embedding with Dirichlet Distribution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To address the above issues simultaneously, we propose a novel model named DiriE, which embeds entities as Dirichlet distributions and relations as multinomial distributions. |
Feiyang Wang; Zhongbao Zhang; Li Sun; Junda Ye; Yang Yan; |
| 300 | Accurate and Explainable Recommendation Via Review Rationalization Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a Recommendation via Review Rationalization (R3) method including 1) a rationale generator to extract rationales from reviews to alleviate the effects of spurious correlations; 2) a rationale predictor to predict user ratings on items only from generated rationales; and 3) a correlation predictor upon both rationales and correlational features to ensure conditional independence between spurious correlations and rating predictions given causal rationales. |
Sicheng Pan; Dongsheng Li; Hansu Gu; Tun Lu; Xufang Luo; Ning Gu; |
| 301 | EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. |
Sheo Yon Jhin; Jaehoon Lee; Minju Jo; Seungji Kook; Jinsung Jeon; Jihyeon Hyeong; Jayoung Kim; Noseong Park; |
| 302 | Comparative Explanations of Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. |
Aobo Yang; Nan Wang; Renqin Cai; Hongbo Deng; Hongning Wang; |
| 303 | WebFormer: The Web-page Transformer for Structure Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we introduce WebFormer, a Web-page transFormer model for structure information extraction from web documents. |
Qifan Wang; Yi Fang; Anirudh Ravula; Fuli Feng; Xiaojun Quan; Dongfang Liu; |
| 304 | Topological Transduction for Hybrid Few-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The hFSL setting results in a hybrid number of shots per class in each space and aggravates the data scarcity challenge as the number of training samples per class in each space is reduced. To alleviate these challenges, we propose the Task-adaptive Topological Transduction Network, namely TopoNet, which trains a heterogeneous graph-based transductive meta-learner that can combine information from both labeled and unlabeled data to enrich the knowledge about the task-specific data distribution and multi-space relationships. |
Jiayi Chen; Aidong Zhang; |
| 305 | Topic Discovery Via Latent Space Clustering of Pretrained Language Model Representations Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we begin by analyzing the challenges of using PLM representations for topic discovery, and then propose a joint latent space learning and clustering framework built upon PLM embeddings. |
Yu Meng; Yunyi Zhang; Jiaxin Huang; Yu Zhang; Jiawei Han; |
| 306 | OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study the attribute mining problem in an open-world setting to extract novel attributes and their values. |
Xinyang Zhang; Chenwei Zhang; Xian Li; Xin Luna Dong; Jingbo Shang; Christos Faloutsos; Jiawei Han; |
| 307 | Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: To train a classifier that calculates the similarity score between a document and a label, we propose a novel metadata-induced contrastive learning (MICoL) method. |
Yu Zhang; Zhihong Shen; Chieh-Han Wu; Boya Xie; Junheng Hao; Ye-Yi Wang; Kuansan Wang; Jiawei Han; |
| 308 | CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a general probabilistic multi-view forecasting framework CAMul, which can learn representations and uncertainty from diverse data sources. |
Harshavardhan Kamarthi; Lingkai Kong; Alexander Rodriguez; Chao Zhang; B Aditya Prakash; |
| 309 | Using Survival Models to Estimate User Engagement in Online Experiments Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we examine long-term user engagement outcomes as a time-to-event problem and demonstrate the use of survival models for estimating long-term effects. |
Praveen Chandar; Brian St. Thomas; Lucas Maystre; Vijay Pappu; Roberto Sanchis-Ojeda; Tiffany Wu; Ben Carterette; Mounia Lalmas; Tony Jebara; |
| 310 | Identifying The Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. |
Maxwell Weinzierl; Sanda Harabagiu; |
| 311 | Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel TWo-side Interactive NetworkS (TWINS) for live broadcast recommendation. |
Jiarui Jin; Xianyu Chen; Yuanbo Chen; Weinan Zhang; Renting Rui; Zaifan Jiang; Zhewen Su; Yong Yu; |
| 312 | STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we propose a spatiotemporal aggregation method STAM to efficiently incorporate temporal information into neighbor embedding learning. |
Zhen Yang; Ming Ding; Bin Xu; Hongxia Yang; Jie Tang; |
| 313 | Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a model-based explainable recommendation approach, i.e., NS-ICF, which stands for Neuro-Symbolic Interpretable Collaborative Filtering. |
Wei Zhang; Junbing Yan; Zhuo Wang; Jianyong Wang; |
| 314 | A Contrastive Sharing Model for Multi-Task Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, as all the subnets are optimized in parallel for each task independently, it is faced with the problem of conflict between parameter gradient updates (i.e, parameter conflict problem). To address this challenge, we propose a novel Contrastive Sharing Recommendation model in MTL learning (CSRec). |
Ting Bai; Yudong Xiao; Bin Wu; Guojun Yang; Hongyong Yu; Jian-Yun Nie; |
| 315 | GRAND+: Scalable Graph Random Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. |
Wenzheng Feng; Yuxiao Dong; Tinglin Huang; Ziqi Yin; Xu Cheng; Evgeny Kharlamov; Jie Tang; |
| 316 | Interpreting BERT-based Text Similarity Via Activation and Saliency Maps Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we present an unsupervised technique for explaining paragraph similarities inferred by pre-trained BERT models. |
Itzik Malkiel; Dvir Ginzburg; Oren Barkan; Avi Caciularu; Jonathan Weill; Noam Koenigstein; |
| 317 | Socially-Equitable Interactive Graph Information Fusion-based Prediction for Urban Dockless E-Scooter Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite its early commercial success, conventional mobility demand and supply prediction based on machine learning and subsequent redistribution may favor advantaged socio-economic communities and tourist regions, at the expense of reducing mobility accessibility and resource allocation for historically disadvantaged communities. To address this unfairness, we propose a socially-Equitable Interactive Graph information fusion-based mobility flow prediction system for Dockless E-scooter Sharing (EIGDES). |
Suining He; Kang G. Shin; |
| 318 | MagNet: Cooperative Edge Caching By Automatic Content Congregating Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we formulate a typical cooperative edge caching problem and propose the MagNet, a decentralized and cooperative edge caching system to address these two challenges. |
Junkun Peng; Qing Li; Xiaoteng Ma; Yong Jiang; Yutao Dong; Chuang Hu; Meng Chen; |
| 319 | Learning-based Fuzzy Bitrate Matching at The Edge for Adaptive Video Streaming Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Although some related techniques (e.g., transcoding and prefetching) are proposed to improve edge services, they cannot fully utilize cached videos. Therefore, we propose a Learning-based Fuzzy Bitrate Matching scheme (LFBM) at the edge for adaptive video streaming, which utilizes the capacity of network and edge servers. |
Wanxin Shi; Qing Li; Chao Wang; Longhao Zou; Gengbiao Shen; Pei Zhang; Yong Jiang; |
| 320 | A Comprehensive Benchmark of Deep Learning Libraries on Mobile Devices Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we first build a comprehensive benchmark that includes 6 representative DL libs and 15 diversified DL models. We then perform extensive experiments on 10 mobile devices, which help reveal a complete landscape of the current mobile DL libs ecosystem. |
Qiyang Zhang; Xiang Li; Xiangying Che; Xiao Ma; Ao Zhou; Mengwei Xu; Shangguang Wang; Yun Ma; Xuanzhe Liu; |
| 321 | Beyond The First Law of Geography: Learning Representations of Satellite Imagery By Leveraging Point-of-Interests Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing studies on unsupervised representation learning for satellite images only take into account the images’ geographic information, ignoring human activity factors. To bridge this gap, we propose using Point-of-Interest (POI) data to capture human factors and design a contrastive learning-based framework to consolidate the representation of satellite imagery with POI information. |
Yanxin Xi; Tong Li; Huandong Wang; Yong Li; Sasu Tarkoma; Pan Hui; |
| 322 | Multi-dimensional Probabilistic Regression Over Imprecise Data Streams Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we investigate solutions for conducting multi-dimensional and multi-granularity probabilistic regression for the imprecise streaming data. |
Ran Gao; Xike Xie; Kai Zou; Torben Bach Pedersen; |
| 323 | Lie to Me: Abusing The Mobile Content Sharing Service for Fun and Profit Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we observe a new Fake-Share attack that adversaries can abuse existing content sharing services to manipulate the displayed source of shared content to bypass the content review of targeted Online Social Apps (OSAs) and induce users to click on the shared fraudulent content. |
Guosheng Xu; Siyi Li; Hao Zhou; Shucen Liu; Yutian Tang; Li Li; Xiapu Luo; Xusheng Xiao; Guoai Xu; Haoyu Wang; |
| 324 | Knowledge Enhanced GAN for IoT Traffic Generation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we take the first step to generate large-scale IoT traffic via a knowledge-enhanced generative adversarial network (GAN) framework, which introduces both the semantic knowledge (e.g., location and environment information) and the network structure knowledge for various IoT devices via a knowledge graph. |
Shuodi Hui; Huandong Wang; Zhenhua Wang; Xinghao Yang; Zhongjin Liu; Depeng Jin; Yong Li; |
| 325 | LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A parallel implementation of SL has great potential in reducing latency, yet existing parallel SL algorithms resort to compromising scalability and/or convergence speed. Motivated by this, the goal of this article is to develop a scalable parallel SL algorithm with fast convergence and low latency. |
Seungeun Oh; Jihong Park; Praneeth Vepakomma; Sihun Baek; Ramesh Raskar; Mehdi Bennis; Seong-Lyun Kim; |
| 326 | Commutativity-guaranteed Docker Image Reconstruction Towards Effective Layer Sharing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose to reconstruct the docker images to raise the number of identical layers and thereby reduce storage and network consumption. |
Sisi Li; Ao Zhou; Xiao Ma; Mengwei Xu; Shangguang Wang; |
| 327 | FingFormer: Contrastive Graph-based Finger Operation Transformer for Unsupervised Mobile Game Bot Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In detail, we introduce a Transformer-style detection model, namely FingFormer. |
Wenbin Li; Xiaokai Chu; Yueyang Su; Di Yao; Shiwei Zhao; Runze Wu; Shize Zhang; Jianrong Tao; Hao Deng; Jingping Bi; |
| 328 | Large-scale Personalized Video Game Recommendation Via Social-aware Contextualized Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Last but not least, it is problematic to use social connections directly for game recommendations due to the massive noise within social connections. To this end, we propose a Social-aware Contextualized Graph Neural Recommender System (SCGRec), which harnesses three perspectives to improve game recommendation. |
Liangwei Yang; Zhiwei Liu; Yu Wang; Chen Wang; Ziwei Fan; Philip S. Yu; |
| 329 | Winning Tracker: A New Model for Real-time Winning Prediction in MOBA Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Existing research is difficult to solve this problem in a dynamic, comprehensive and systematic way. In this study, we design a unified framework, namely Winning Tracker (WT), for solving this problem. |
Chuang Zhao; Hongke Zhao; Yong Ge; Runze Wu; Xudong Shen; |
| 330 | Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided Masking Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: A worth noting property within raw player behavioral data is that a lot of it is task-irrelevant. For these data characteristics, we introduce a BPE-enhanced compression method and propose a novel adaptive masking strategy called Masking by Token Confidence (MTC) for the Masked Language Modeling (MLM) pre-training task. |
Jiashu Pu; Jianshi Lin; Xiaoxi Mao; Jianrong Tao; Xudong Shen; Yue Shang; Runze Wu; |
| 331 | Nebula: Reliable Low-latency Video Transmission for Mobile Cloud Gaming Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper introduces , an end-to-end cloud gaming framework to minimize the impact of network conditions on the user experience. |
Ahmad Alhilal; Tristan Braud; Bo Han; Pan Hui; |
| 332 | Analyzing The Differences Between Professional and Amateur Esports Through Win Probability Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Using interpretable win probability models trained on large CSGO data sets, we improve upon the current state of the art in Counter-Strike: Global Offensive (CSGO) win probability prediction. |
Peter Xenopoulos; William Robert Freeman; Claudio Silva; |
| 333 | DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: However, the complexity of drafting induces difficulties for beginners to choose the appropriate characters based on the characters of their team while considering their own champion preferences. To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player’s champion preferences and the interaction between the players. |
Hojoon Lee; Dongyoon Hwang; Hyunseung Kim; Byungkun Lee; Jaegul Choo; |
| 334 | The Price to Play: A Privacy Analysis of Free and Paid Games in The Android Ecosystem Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we aim to shine a light on the tracking ecosystem in mobile games on Android and understand how different monetization models can impact user privacy. |
Pierre Laperdrix; Naif Mehanna; Antonin Durey; Walter Rudametkin; |
| 335 | On The Origins Of Hypertext In The Disasters Of The Short 20th Century Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: That story is true, but it is not complete: hypertext and the Web are also built on a foundation of ideas. Specifically, I believe the Web we know arose from ideas rooted in the disasters of the short twentieth century, 1914–1989. |
Mark Bernstein; |
| 336 | Through The Lens of The Web Conference Series: A Look Into The History of The Web Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we focus on the small story within the great one of the Web. |
Damien Graux; Fabrizio Orlandi; |
| 337 | From Indymedia to Tahrir Square: The Revolutionary Origins of Status Updates on Twitter Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this essay, we trace the origin of the status update for spreading news from protest-driven community networks like Indymedia and text messages for protest coordination via TxtMob. |
Harry Halpin; Evan Henshaw-Plath; |
| 338 | “Way Back Then”: A Data-driven View of 25+ Years of Web Evolution Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We study the changes in popularity, from Geocities and Yahoo! in the mid-to-late 1990s to the likes of Google, Facebook, and Tiktok of today. |
Vibhor Agarwal; Nishanth Sastry; |
| 339 | A Never-Ending Project for Humanity Called “the Web” Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper we summarized the main historical steps in making the Web, its foundational principles and its evolution. |
Fabien Gandon; Wendy Hall; |
| 340 | Fairness Audit of Machine Learning Models with Confidential Computing Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. |
Saerom Park; Seongmin Kim; Yeon-sup Lim; |
| 341 | BZNet: Unsupervised Multi-scale Branch Zooming Network for Detecting Low-quality Deepfake Videos Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We propose a novel LQ DF detection architecture, multi-scale Branch Zooming Network (BZNet), which adopts an unsupervised super-resolution (SR) technique and utilizes multi-scale images for training. |
Sangyup Lee; Jaeju An; Simon S. Woo; |
| 342 | VICTOR: An Implicit Approach to Mitigate Misinformation Via Continuous Verification Reading Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We design and evaluate VICTOR, an easy-to-apply module on top of a recommender system to mitigate misinformation. |
Kuan-Chieh Lo; Shih-Chieh Dai; Aiping Xiong; Jing Jiang; Lun-Wei Ku; |
| 343 | End-to-End Learning for Fair Ranking Systems Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the predicted rankings. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. |
James Kotary; Ferdinando Fioretto; Pascal Van Hentenryck; Ziwei Zhu; |
| 344 | To Trust or Not To Trust: How A Conversational Interface Affects Trust in A Decision Support System Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Despite the growing applications of conversational user interfaces (CUIs), little is currently understood about the suitability of such interfaces for decision support and how CUIs inspire trust among humans engaging with decision support systems. In this work, we aim to address this gap and answer the following question: to what extent can a conversational interface build user trust in decision support systems in comparison to a conventional graphical user interface? |
Akshit Gupta; Debadeep Basu; Ramya Ghantasala; Sihang Qiu; Ujwal Gadiraju; |
| 345 | Link Recommendations for PageRank Fairness Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Network algorithms play a critical role in various applications, such as recommendations, diffusion maximization, and web search. In this paper, we focus on the fairness of such algorithms and in particular of PageRank. |
Sotiris Tsioutsiouliklis; Evaggelia Pitoura; Konstantinos Semertzidis; Panayiotis Tsaparas; |
| 346 | Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we study a novel and practical problem of learning privacy-preserving GNNs with partially observed sensitive attributes. |
Hui Hu; Lu Cheng; Jayden Parker Vap; Mike Borowczak; |
| 347 | Causal Representation Learning for Out-of-Distribution Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we consider the Out-Of-Distribution (OOD) recommendation problem in an OOD environment with user feature shifts. |
Wenjie Wang; Xinyu Lin; Fuli Feng; Xiangnan He; Min Lin; Tat-Seng Chua; |
| 348 | Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this study, we propose a privacy-preserving training algorithm for a fair support vector machine classifier based on Homomorphic Encryption (HE), where the privacy of both sensitive information and model secrecy can be preserved. |
Saerom Park; Junyoung Byun; Joohee Lee; |
| 349 | Regulating Online Political Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: We first provide a theoretical model and deliver key insights that can be used to regulate online ad auctions for political ads, and analyze the implications of the proposed interventions empirically. We characterize the optimal auction mechanisms where the regulator takes into account both the ad revenues collected and societal objectives (such as the share of ads allocated to politicians, or the prices paid by them). |
Eray Turkel; |
| 350 | Generating Perturbation-based Explanations with Robustness to Out-of-Distribution Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This work addresses the OoD issue by designing a simple yet effective module that can quantify the affinity between the perturbed data and the original dataset distribution. |
Luyu Qiu; Yi Yang; Caleb Chen Cao; Yueyuan Zheng; Hilary Ngai; Janet Hsiao; Lei Chen; |
| 351 | Distributionally-robust Recommendations for Improving Worst-case User Experience Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In a simulation study, we demonstrate that such performance gap among various user groups is enlarged by an ERM-trained recommender in the long-term. To mitigate such amplification effects, we propose to optimize for the worst-case performance under the Distributionally Robust Optimization (DRO) framework, with the goal of improving long-term fairness for disadvantaged subgroups. |
Hongyi Wen; Xinyang Yi; Tiansheng Yao; Jiaxi Tang; Lichan Hong; Ed H. Chi; |
| 352 | Can I Only Share My Eyes? A Web Crowdsourcing Based Face Partition Approach Towards Privacy-Aware Face Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we focus on a privacy-aware face recognition problem where the goal is to empower the facial applications to train their face recognition models with images shared by social media users while protecting the identity of the users. |
Ziyi Kou; Lanyu Shang; Yang Zhang; Siyu Duan; Dong Wang; |
| 353 | A Duo-generative Approach to Explainable Multimodal COVID-19 Misinformation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a generative approach to detect multimodal COVID-19 misinformation by investigating the cross-modal association between the visual and textual content that is deeply embedded in the multimodal news content. |
Lanyu Shang; Ziyi Kou; Yang Zhang; Dong Wang; |
| 354 | Domain Adaptive Fake News Detection Via Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we address the limitations of existing automated fake news detection models by incorporating auxiliary information (e.g., user comments and user-news interactions) into a novel reinforcement learning-based model called REinforced Adaptive Learning Fake News Detection (REAL-FND). |
Ahmadreza Mosallanezhad; Mansooreh Karami; Kai Shu; Michelle V. Mancenido; Huan Liu; |
| 355 | Towards An Interpretable Approach to Classify and Summarize Crisis Events from Microblogs Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets. |
Thi Huyen Nguyen; Koustav Rudra; |
| 356 | On Explaining Multimodal Hateful Meme Detection Models Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i.e., image and text) of the hateful memes. To fill this research gap, this paper propose three research questions to improve our understanding of these visual-linguistic models performing the hateful meme classification task. |
Ming Shan Hee; Roy Ka-Wei Lee; Wen-Haw Chong; |
| 357 | Hate Speech in The Political Discourse on Social Media: Disparities Across Parties, Gender, and Ethnicity Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this work, we empirically analyze how the amount of hate speech in replies to posts from politicians on Twitter depends on personal characteristics, such as their party affiliation, gender, and ethnicity. |
Kirill Solovev; Nicolas Pröllochs; |
| 358 | Exposing Query Identification for Search Transparency Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Overall, our work contributes a novel conception of transparency in search systems and computational means of achieving it. |
Ruohan Li; Jianxiang Li; Bhaskar Mitra; Fernando Diaz; Asia J. Biega; |
| 359 | Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we propose a novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news. |
Shoujin Wang; Xiaofei Xu; Xiuzhen Zhang; Yan Wang; Wenzhuo Song; |
| 360 | “This Is Fake! Shared It By Mistake”:Assessing The Intent of Fake News Spreaders Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Our aim in this work is to assess the intent of fake news spreaders. |
Xinyi Zhou; Kai Shu; Vir V. Phoha; Huan Liu; Reza Zafarani; |
| 361 | Alexa, in You, I Trust! Fairness and Interpretability Issues in E-commerce Search Through Smart Speakers Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we ask whether the explanation presented for a product selection by the Alexa VA installed on an Amazon Echo device is consistent with human understanding as well as with the observations on other traditional mediums (e.g., desktop e-commerce search). |
Abhisek Dash; Abhijnan Chakraborty; Saptarshi Ghosh; Animesh Mukherjee; Krishna P. Gummadi; |
| 362 | Moral Emotions Shape The Virality of COVID-19 Misinformation on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: Here we analyze a large sample consisting of COVID-19 rumor cascades from Twitter that have been fact-checked by third-party organizations. |
Kirill Solovev; Nicolas Pröllochs; |
| 363 | Fostering Engagement of Underserved Communities with Credible Health Information on Social Media Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: This presents unique challenges in engaging low-literate communities that live in poverty and lack access to the Internet. We describe the design and deployment of a voice-based social media platform, accessible over simple phones, for actively engaging such communities in Pakistan with reliable COVID information. |
Agha Ali Raza; Mustafa Naseem; Namoos Hayat Qasmi; Shan Randhawa; Fizzah Malik; Behzad Taimur; Sacha St-Onge Ahmad; Sarojini Hirshleifer; Arman Rezaee; Aditya Vashistha; |
| 364 | Screenshots, Symbols, and Personal Thoughts: The Role of Instagram for Social Activism Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we highlight the use of Instagram for social activism, taking 2019 Hong Kong protests as a case study. |
Ehsan-Ul Haq; Tristan Braud; Yui-Pan Yau; Lik-Hang Lee; Franziska B. Keller; Pan Hui; |
| 365 | ExpScore: Learning Metrics for Recommendation Explanation Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we present a large explanation dataset named RecoExp, which contains thousands of crowdsourced ratings of perceived quality in explaining recommendations. |
Bingbing Wen; Yunhe Feng; Yongfeng Zhang; Chirag Shah; |
| 366 | Following Good Examples – Health Goal-Oriented Food Recommendation Based on Behavior Data Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: In this paper, we build a food recommendation system that can be used on the web or in a mobile app to help users meet their goals on body weight, while also taking into account their health information (BMI) and the nutrition information of foods (calories). |
Yabo Ling; Jian-Yun Nie; Daiva Nielsen; Bärbel Knäuper; Nathan Yang; Laurette Dubé; |
| 367 | Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse Using Label Refinement Related Papers Related Patents Related Grants Related Orgs Related Experts View Highlight: The weak labels can be inaccurate at the article or social media post level where the stance of the user does not align with the news source or article credibility. We propose a framework to use a detection model self-trained on the initial weak labels with uncertainty sampling based on entropy in predictions of the model to identify potentially inaccurate labels and correct for them using self-supervision or relabeling. |
Karishma Sharma; Emilio Ferrara; Yan Liu; |