Paper abstract

Learning Bidirectional Similarity for Collaborative Filtering

Bin Cao - Hong Kong University of Science and Technology, Hong Kong
Jian-Tao Sun - Microsoft Research Asia, China
Jianmin Wu - Microsoft Research Asia, China
Qiang Yang - Hong Kong University of Science and Technology, Hong Kong
Zheng Chen - Microsoft Research Asia, China

Session: Matrix Factorization
Springer Link:

Memory-based collaborative filtering aims at predicting the utility of a certain item for a particular user based on the previous ratings from similar users and similar items. Previous studies in finding similar users and items are based on user-defined similarity metrics such as Pearson Correlation Coefficient or Vector Space Similarity which are not adaptive and optimized for different applications and datasets. Moreover, previous studies have treated the similarity function calculation between users and items separately. In this paper, we propose a novel adaptive bidirectional similarity metric for collaborative filtering. We automatically learn similarities between users and items simultaneously through matrix factorization. We show that our model naturally extends the memory based approaches. Theoretical analysis shows our model to be a novel generalization of the SVD model. We evaluate our method using three benchmark datasets, including MovieLens, EachMovie and Netflix, through which we show that our methods outperform many previous baselines.