Aspect-based latent factor model by integrating ratings and reviews for recommender system

摘要

Recommender system has been recognized as a superior way for solving personal information overload problem. Rating, as an evaluation criteria revealing how much a customer likes a product, has been a foundation of recommender systems for a long period based on the popular latent factor models. However, review texts as the valuable user generated content have been neglected all the time. Recently, models integrating ratings and review texts as training sources have attracted a lot of attention, which may model review texts by topic model or its variants and then link latent factor vectors to topic distribution of review texts. For that, the integrated models need complicated optimization algorithms to fuse the heterogeneous sources, that may cause greater errors.