Leveraging Ratings and Reviews with Gating Mechanism for Recommendation
Haifeng Xia, Zengmao Wang, Bo Du, Lefei Zhang, Shuai Chen, Gang Chun · 2019
Recommender system plays an important role to provide people with personalized information based on their history records. However, it is still a challenge to capture the preference of users accurately due to the sparsity of rating data and the heterogeneity of review data. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns latent representations from ratings and reviews. Specifically, the model learns the rating feature and textual feature based on ratings and reviews simultaneously. Two embedding layers are employed to learn rating feature for users and items based on the user and item interactions, and two attention-based GRU networks learn context-aware representation from user and item reviews. Then a gating mechanism is used to leverage contributions from rating feature and textual feature. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods. Moreover, the keywords in reviews can be highlighted to interpret the predictions with the attention mechanism.