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Poster
Mixture-Rank Matrix Approximation for Collaborative Filtering
Dongsheng Li · Chao Chen · Wei Liu · Tun Lu · Ning Gu · Stephen Chu

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #47 #None
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Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among today's collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item feature matrices is typically fixed, i.e., the same rank is adopted to describe all users/items. However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy. In this paper, a mixture-rank matrix approximation (MRMA) method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks. Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA. Experimental studies on MovieLens and Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

Keywords: Recommender Systems · Matrix and Tensor Factorization · Collaborative Filtering

Author Information

Dongsheng Li (IBM Research - China)
Kehan Chen (Tongji University)
Wei Liu (Tencent AI Lab)
Tun Lu (Fudan University)
Ning Gu (Fudan University)
Stephen Chu (IBM Research - China)

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