A Graph-Based Method for Combining Collaborative and Content-Based Filtering

Collaborative filtering and content-based filtering are two main approaches to make recommendations in recommender systems. While each approach has its own strengths and weaknesses, combining the two approaches can improve recommendation accuracy. In this paper, we present a graph-based method that allows combining content information and rating information in a natural way. The proposed method uses user ratings and content descriptions to infer user-content links, and then provides recommendations by exploiting these new links in combination with user-item links. We present experimental results showing that the proposed method performs better than a pure collaborative filtering, a pure content-based filtering, and a hybrid method.