Supervised Topic Model with Consideration of User and Item

In this paper, we propose a new supervised topic model by incorporating the user and the item information. The proposed model can simultaneously utilize the textual topic and user-item factors for label prediction. We conduct prediction experiment with a public review dataset. The results demonstrate the advantages of our model. It shows clear improvement compared with traditional supervised topic model and recommendation method.