A Bayesian Recommender Model for User Rating and Review Profiling

作     者：Mingming Jiang Dandan Song Lejian Liao Feida Zhu 

作者机构：School of Computer Science and TechnologyBeijing Institute of Technology School of Information SystemsSingapore Management University 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2015年第20卷第6期

页      面：634-643页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：supported by the National Key Basic Research and Development (973) Program of China (No. 2013CB329600) the National Natural Science Foundation of China (Nos. 61472040 and 60873237) Beijing Higher Education Young Elite Teacher Project (No. YETP1198) 

主　　题：collaborative filtering topic model recommender sy 

摘      要：Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles’ information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering(CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with user attitudes (i.e., abstract rating patterns) over the same distribution, our method achieves greater accuracy than the traditional approach on the rating prediction task. Moreover, with review text information involved, latent user rating attitudes are interpretable and cold-start problem can be *** property qualifies our method for serving as a recommender task with very sparse datasets. Furthermore,unlike most related works, we treat each review as a document, not all reviews of each user or item together as one document, to fully exploit the reviews’ information. Experimental results on 25 real-world datasets demonstrate the superiority of our model over state-of-the-art methods.