The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Hypergraph Contrastive Collaborative Filtering
Open accessAbstract
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, in comprehensively capturing the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. The implementation codes are available at https://github.com/akaxlh/HCCF.
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Cites
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Neural Collaborative Filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052569 · Cited by 6,652
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401063 · Cited by 4,430
Neural Graph Collaborative Filtering
Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331267 · Cited by 3,823
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219890 · Cited by 3,733
Feng, Y., You, H., Zhang, Z., Ji, R., & Gao, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013558 · Cited by 1,895
Self-supervised Graph Learning for Recommendation
Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462862 · Cited by 1,488
AutoRec: Autoencoders Meet Collaborative Filtering
Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2740908.2742726 · Cited by 1,175
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Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
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Cited by
Self-supervised learning for recommender systems: A survey
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3282907 · Cited by 413
Disentangled Contrastive Collaborative Filtering
Ren, X., Xia, L., Zhao, J., Yin, D., & Huang, C. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591665 · Cited by 171
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Metadata
- Authors
- Xia, L., Huang, C., Xu, Y., Zhao, J., Yin, D., & Huang, J.
- Venue
- Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2022-07-06
- Pages
- 70-79
- OpenAlex
- W4224983022
- Cited by
- 445
- References
- 29
- FWCI
- 88.67
- Type
- conference paper
- Language
- en
- Open access
- green
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