Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 6th International Conference on Computing and Data Science
Series Vol. 64 , 15 May 2024
Open
Access | Article
Application of graph modeling and contrast learning in recommender system
* Author to whom correspondence should be addressed.
Abstract
With the wide application of personalized recommender system in various fields, how to improve the accuracy and personalized level of recommender system has become a research hotspot. In this paper, a method of combining graph modeling and contrast learning is proposed to improve the performance of recommendation system by mining complex user project interaction and user preference. We first construct the user-project interaction graph, and extract the features of the graph structure by graph neural network (GNN) . In particular, graph convolution network (GCN) is used to update the node representation, and comparative learning is introduced to optimize the feature representation so as to improve the accuracy and personalization of recommendation. The experimental results show that the proposed method is superior to the traditional method in accuracy, recall and F 1 score. By analyzing the mechanism of combining graph modeling and contrast learning, this paper further expounds the theoretical basis and practical application of improving the performance of recommender system, and points out the limitations of existing methods and the future research direction.
Keywords
Recommendation system, graph modeling, contrast learning, graph convolution network (GCN), feature extraction.
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 6th International Conference on Computing and Data Science
- ISBN (Print)
- 978-1-83558-425-5
- ISBN (Online)
- 978-1-83558-426-2
- Published Date
- 15 May 2024
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/64/20241375
- Copyright
- 15 May 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited