Learning Bidirectional Similarity for Collaborative Filtering

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.

fulltext.pdf
PDF file

In  ECML PKDD '08: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases

Publisher  Springer-Verlag
©Springer

Details

TypeInproceedings
URLhttp://dx.doi.org/10.1007/978-3-540-87479-9_30
Pages178–194
ISBN978-3-540-87478-2
AddressBerlin, Heidelberg
> Publications > Learning Bidirectional Similarity for Collaborative Filtering