Topicmf: Simultaneously exploiting ratings and reviews for recommendation

Although users ’ preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender mod-els. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users ’ preference, but ignore the review texts accom-panied with rating information. In this paper, we pro-pose a novel matrix factorization model (called Top-icMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its ef-fectiveness for recommendation tasks