Collaborative recommendation with user generated content

In the age of Web 2.0, user generated content (UGC), such as user review and social tag, ubiquitously exists on the Internet. Although there exist different kinds of UGC in recommender systems, the existing works only studied a single kind of UGC in each of their papers. Thus, the previous works lose a chance to uncover the similar effects of different kinds of UGC in recommender systems. In this paper, we propose a unified way to utilize various types of UGC to enhance the recommendation accuracy. We build two novel statistical models, which are based on collaborative filtering and topic modeling. Incorporating UGC text, one model focuses on learning user preferences, and the other model aims to learn user preferences and item aspects jointly. With an effective parameter estimation algorithm, our models can not only acquire prediction values of missing ratings, but also produce interpretable topics. We conducted comprehensive experiments on three real-world datasets. The experimental results demonstrate that our proposed models can achieve large improvements compared to several well-known baseline models.