Joint Representation Learning with Ratings and Reviews for Recommendation

Abstract Recommender system is an important technique to find the information that the users may be interested by their feedbacks. However, it is still a challenge to model the preference of users due to the sparsity of user feedbacks. To alleviate this problem, many methods are developed by extracting information from various kinds of auxiliary information that are related to the users. In the auxiliary information, review is the popular one, since it can reflect both user preferences and item characteristics. Moreover, the review can generate plausible recommendation explanations in the recommendation results. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns rating embedding and textural feature from ratings and reviews respectively. Specifically, two embedding layers are employed to learn rating embedding for users and items based on the interactions, and two attention-based GRU networks attempt to learn context-aware representation as textural feature for users and items from reviews. To leverage the contribution between rating embedding and textual feature and obtain the fused features for users and items, a proposed gating mechanism is used. Then an interaction-learning layer is adopted to learn the user and item interaction information based on the fused user and item features. The prediction score is obtained with the factorization machine. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods.