Learning to Rank Features for Recommendation over Multiple Categories

Abstract

Incorporating phrase-level sentiment analysis on users' textual reviews for recommendation has became a popular meth-od due to its explainable property for latent features and high prediction accuracy. However, the inherent limitations of the existing model make it difficult to (1) effectively distinguish the features that are most interesting to users, (2) maintain the recommendation performance especially when the set of items is scaled up to multiple categories, and (3) model users' implicit feedbacks on the product features. In this paper, motivated by these shortcomings, we first introduce a tensor matrix factorization algorithm to Learn to Rank user Preferences based on Phrase-level sentiment analysis across Multiple categories (LRPPM for short), and then by combining this technique with Collaborative Filtering (CF) method, we propose a novel model called LRPPM-CF to boost the performance of recommendation. Thorough experiments on two real-world datasets demonstrate that our proposed model is able to improve the performance in the tasks of capturing users' interested features and item recommendation by about 17%-24% and 7%-13%, respectively, as compared with several state-of-the-art methods.