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...

• Type: Journal
• Year: 2012
• Where: SDM
• Authors: ErHeng Zhong, Wei Fan, Qiang Yang
• Keywords: Art Algorithms, Data Mining, Heterogeneous Environments, Matrix Factorization, SDM 2012