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Contextual Collaborative Filtering via Hierarchical Matrix Factorization

7 years 10 months ago
Contextual Collaborative Filtering via Hierarchical Matrix Factorization
Matrix factorization (MF) has been demonstrated to be one of the most competitive techniques for collaborative filtering. However, state-of-the-art MFs do not consider contextual information, where ratings can be generated under different environments. For example, users select items under various situations, such as happy mood vs. sad, mobile vs. stationary, movies vs. book, etc. Under different contexts, the preference of users are inherently different. The problem is that MF methods uniformly decompose the rating matrix, and thus they are unable to factorize for different contexts. To amend this problem and improve recommendation accuracy, we introduce a “hierarchical” factorization model by considering the local context when performing matrix factorization. The intuition is that: as ratings are being generated from heterogeneous environments, certain user and item pairs tend to be more similar to each other than others, and hence they ought to receive more collaborative i...
ErHeng Zhong, Wei Fan, Qiang Yang
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where SDM
Authors ErHeng Zhong, Wei Fan, Qiang Yang
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