Multi-Behavioral Sequential Recommendation
Pages 902 - 906
Abstract
Sequential recommendation models are crucial for next-item prediction tasks in various online platforms, yet many focus on a single behavior, neglecting valuable implicit interactions. While multi-behavioral models address this using graph-based approaches, they often fail to capture sequential patterns simultaneously. Our proposed Multi-Behavioral Sequential Recommendation framework (MBSRec) captures the multi-behavior dependencies between the heterogeneous historical interactions via multi-head self-attention. Furthermore, we utilize a weighted binary cross-entropy loss for precise behavior control. Experimental results on four datasets demonstrate MBSRec’s significant outperformance of state-of-the-art approaches. The implementation code is available here 1.
References
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Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li. 2022. Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2263–2274.
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Index Terms
- Multi-Behavioral Sequential Recommendation
Recommendations
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningLearning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item ...
Exploiting User and Item Attributes for Sequential Recommendation
Neural Information ProcessingAbstractThis paper exploits both the user and item attribute information for sequential recommendation. Attribute information has been explored in a number of traditional recommendation systems and proved to be effective to enhance the recommend ...
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data MiningSequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resourceful sequential patterns embedded in the behavior history which reveal the ...
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Published In
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
Copyright © 2024 ACM.
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- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Published: 08 October 2024
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