Bilinear models for item recommendation based on tags

Recently, collaborative tagging has been gaining increasing popularity in a large variety of websites. The tags generated from collaborative tagging provide highly abstracted information about users´ personal taste on information item, and therefore could be used to profile both users and items. However, flattening the three dimensional user-tag-item matrix into two-way matrices will lead to loss of two dimensional relationships between users, items and tags completely. In this paper, we use a predictive bilinear model to capture the informative interaction patterns among user-tag and item-tag matrices, and employ the Nonnegative Matrix Factorization (NMF) algorithm to extract lower dimensional representative features from tags. Experiments on two real-world datasets show that our approach substantially outperforms the traditional CF methods as well as tag-based recommendation methods reported in the literature.