Information-Controllable Graph Contrastive Learning for Recommendation
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- Information-Controllable Graph Contrastive Learning for Recommendation
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A Review-aware Graph Contrastive Learning Framework for Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalMost modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the ...
SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data MiningContrastive-learning-based neural networks have recently been introduced to recommender systems, due to their unique advantage of injecting collaborative signals to model deep representations, and the self-supervision nature in the learning process. ...
Multi-view graph contrastive representation learning for bundle recommendation
AbstractBundle recommendation can recommend a collection of associated items that can be consumed together to a user rather than recommending these items separately, making it extremely suitable for some scenarios such as product bundle recommendation ...
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
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