User-Personalized Review Rating Prediction Method Based On Review Text Content And User-Item Rating Matrix

With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user's personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.