A Regularized Recommendation Algorithm with Probabilistic Sentiment-Ratings

Recommender systems (RS) provide personalized suggestions based on users´ past behavior and/or similarities between users´ and products´ profiles. Although we observed a high interest in the research community over RS algorithms these commonly overlook users´ opinions. In this paper, we research the inclusion of sentiment knowledge in RS to improve the overall quality of recommendations. In contrast to similar approaches, we propose a matrix factorization with a new factor to regularize probabilistic ratings. A sentiment analysis algorithm implementing a multiple Bernoulli classification computes these probabilistic ratings. The combination of a regularization factor with probabilistic ratings offers a general framework capable of embedding multiple sources into a theoretical well-founded matrix factorization algorithm. Experiments show that with an evaluation on a dataset with 1.7 million reviews we have successfully introduced a novel approach to incorporate on a RS with inferred rating based in a sentiment analysis framework. Also, replacing explicit ratings by probabilistic inferred ratings the RS performance improves, thus, our proposed framework is able to better accommodate the uncertainty of users explicit rating.