Exploring Review Content for Recommendation via Latent Factor Model

Recommender systems have been widely studied and applied in many real applications such as e-commerce sites, product review sites, and mobile App stores. In these applications, users can provide their feedback towards the items in the form of ratings, and they usually accompany the feedback 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, existing recommendation methods (e.g., collaborative filtering) mainly make use of the historical ratings while ignore the content information. In this paper, we propose to explore the review content for better recommendation via latent factor model. In particular, we propose two strategies to leverage the review content. The first strategy incorporates review content as a guidance term to guide the learnt latent factors of user preferences; the second strategy formulates a regularization term to constrain the preference differences between similar users. Experimental evaluations on two real data sets demonstrate the usefulness of review content and the effectiveness of the proposed method for recommendation.