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- W3208349097 abstract "Session-based recommendation is to predict an anonymous user's next action based on the user's historical actions in the current session. However, the cold-start problem of limited number of actions at the beginning of an anonymous session makes it difficult to model the user's behavior, i.e., hard to capture the user's various and dynamic preferences within the session. This severely affects the accuracy of session-based recommendation. Although some existing meta-learning based approaches have alleviated the cold-start problem by borrowing preferences from other users, they are still weak in modeling the behavior of the current user. To tackle the challenge, we propose a novel cluster-based meta-learning model for session-based recommendation. Specially, we adopt a soft-clustering method and design a parameter gate to better transfer shared knowledge across similar sessions and preserve the characteristics of the session itself. Besides, we apply two self-attention blocks to capture the transition patterns of sessions in both item and feature aspects. Finally, comprehensive experiments are conducted on two real-world datasets and demonstrate the superior performance of CBML over existing approaches." @default.
- W3208349097 created "2021-11-08" @default.
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- W3208349097 date "2021-10-26" @default.
- W3208349097 modified "2023-10-05" @default.
- W3208349097 title "CBML" @default.
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- W3208349097 doi "https://doi.org/10.1145/3459637.3482239" @default.
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