Pay Attention to Attention for Sequential Recommendation
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- Pay Attention to Attention for Sequential Recommendation
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Sequential Recommendation via Stochastic Self-Attention
WWW '22: Proceedings of the ACM Web Conference 2022Sequential recommendation models the dynamics of a user’s previous behaviors in order to forecast the next item, and has drawn a lot of attention. Transformer-based approaches, which embed items as vectors and use dot-product self-attention to measure ...
Attention Mechanism Indicating Item Novelty for Sequential Recommendation
ASONAM '22: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningMost sequential recommendation systems, including those that employ a variety of features and state-of-the-art network models, tend to favor items that are the most popular or of greatest relevance to the historic behavior of the user. Recommendations ...
HSA: Hyperbolic Self-attention for Sequential Recommendation
Web and Big DataAbstractRecently, researchers apply various deep neural networks to the task of sequential recommendation, which captures dynamics of user preference from user behavior data to make accurate recommendation. Self-attention based approaches have been ...
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- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
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- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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