Topic and Sentiment Analysis Matrix Factorization on Rating Prediction for Recommendation

Abstract

In today’s online services, users’ feedback such as numerical rating, textual review, time of purchase and so on for each product is often encouraged to provide. Many recommender systems predict the products that the users may like and purchase in the future using users’ historical ratings. With the increase of user data in the systems, more detailed and interpretable information about product features and user sentiments can be extracted from textual reviews that are relative to ratings. In this paper, we propose a novel topic and sentiment matrix factorization model, which leverages both topic and sentiment drawn from the reviews simultaneously. First, we conduct topic analysis and sentiment analysis on reviews using Latent Dirichlet Allocation (LDA) and a lexicon construction technique, respectively. Second, we combine the user consistency, which is calculated from his/her reviews and ratings, and helpful votes from other users on reviews to obtain a reliability measure to weight the ratings. Third, we integrate the obtained reliability measure and the results of the topic and sentiment analysis of reviews into the matrix factorization framework for prediction. Our experimental comparison using eight Amazon datasets indicates that the proposed method significantly improves performance compared to traditional matrix factorization up to 20.82%.