Learning to Recommend with User Generated Content

In the era of Web 2.0, user generated content (UGC), such as social tag and user review, widely exists on the Internet. However, in recommender systems, most of existing related works only study single kind of UGC in each paper, and different types of UGC are utilized in different ways. This paper proposes a unified way to use different types of UGC to improve the prediction accuracy for recommendation. We build two novel collaborative filtering models based on Matrix Factorization (MF), which are oriented to user features learning and item features learning respectively. In the user side, we construct a novel regularization term which employs UGC to better understand a user’s interest. In the item side, we also construct a novel regularization term to better infer an item’s characteristic. We conduct comprehensive experiments on three real-world datasets, which verify that our models significantly improve the prediction accuracy of missing ratings in recommender systems.