Text Content Enhanced Matrix Factorization And Application In Recommender Systems

Recommender systems have been widely studied in the last decades, some have been applied in many real applications such as mobile App stores, e-commerce sites and movie sites. Most conventional recommender systems focus on users’historic rat-ings over items. However, in real-world systems besides rating, users usually provide their feedback towards the items with a few words (i.e., review content) to justify their ratings. Such review content may contain rich information about user tastes and item characteristics.However, due to the difficulty of text processing (too much noise, divergence of dimension), existing recommendation methods mainly make use of the historical ratings while ignore the content information. In this paper, we propose to take use of the review content alone with ratings for better recommendation via matrix factorization model. In particular, this paper has the following contributions. First, on the base of topic model, we propose the GTRT model using guidance term and regularization term to leverage the nouns in review content and enhance the learning process of user-side matrix. Second, we further extract item emotion features from nouns and modifiers to enhance the learning process of item-side matrix. Finally, we make a prototype system based on the proposed model. Also, experimental evaluations on two real data sets demonstrate the usefulness of review content and the effectiveness of the proposed method for recommendation.