1. Mixture Matrix Approximation for Collaborative Filtering.
- Author
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Li, Dongsheng, Chen, Chao, Lu, Tun, Chu, Stephen M., and Gu, Ning
- Subjects
- *
RECOMMENDER systems , *MIXTURES , *MATRICES (Mathematics) , *TASK analysis , *TOY industry - Abstract
Matrix approximation (MA) methods are integral parts of today's recommender systems. In standard MA methods, only one feature vector is learned for each user/item, which may not be accurate enough to characterize the diverse interests of users/items. For instance, users could have different opinions on a given item, so that they may need different feature vectors for the item to represent their unique interests. To this end, this article proposes a mixture matrix approximation (MMA) method, in which we assume that the user-item ratings follow mixture distributions and the user/item feature vectors vary among different stars to better characterize the diverse interests of users/items. Furthermore, we show that the proposed method can tackle both rating prediction and the top-N recommendation problems. Empirical studies on MovieLens, Netflix and Amazon datasets demonstrate that the proposed method can outperform state-of-the-art MA-based collaborative filtering methods in both rating prediction and top-N recommendation tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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