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- W4210558891 abstract "Traditional robust recommendation methods view atypical user-item interactions as noise and aim to reduce their impact with some kind of noise filtering technique, which often suffers from two challenges. First, in real world, atypical interactions may signal users' temporary interest different from their general preference. Therefore, simply filtering out the atypical interactions as noise may be inappropriate and degrade the personalization of recommendations. Second, it is hard to acquire the temporary interest since there are no explicit supervision signals to indicate whether an interaction is atypical or not. To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users. Particularly, we propose a multi-agent reinforcement learning framework containing a recommender agent and an auxiliary classifier agent, which are jointly trained with the objective of maximizing the cumulative return of the recommendations made by the recommender agent. During the joint training process, the knowledge about learning temporary interest from atypical interactions can be transferred to the recommender agent. At last, the experiments conducted on real world datasets verify effectiveness of TIARec." @default.
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- W4210558891 date "2023-10-01" @default.
- W4210558891 modified "2023-10-15" @default.
- W4210558891 title "Learning From Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning" @default.
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- W4210558891 doi "https://doi.org/10.1109/tkde.2022.3144292" @default.
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