Neural Interactive Collaborative Filtering

4 Jul 2020Lixin ZouLong XiaYulong GuXiangyu ZhaoWeidong LiuJimmy Xiangji HuangDawei Yin

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste drifting... (read more)

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