Collaborative Recommendation Framework Based on Ratings and Textual Reviews
TAN Yunzhi, ZHANG Min, LIU Yiqun, MA Shaoping
State Key Laboratory of Intelligent Technology and Systems, Beijing 100084 Tsinghua National Laboratory for Information Science and Technology, Beijing 100084 Department of Computer Science and Technology, Tsinghua University, Beijing 100084
Abstract The feedback of users usually contains a numeric rating and a textual review. In this paper, textual review information is used to learn the distributions of item features on different topics and the user preference to different features of items. Then, the topic-based user preference similarity is incorporated into the traditional collaborative filtering recommendation systems. A recommendation framework based on ratings and textual reviews is proposed. With the proposed framework, review information can be easily introduced into the existing recommendation algorithms. By employing textual reviews, the problem of data sparsity in the traditional recommendation algorithms is relieved. Experiments are conducted on 22 real-world datasets from Amazon and the experimental results demonstrate the advantages and the effectiveness of the proposed framework.
Fund:Supported by National Basic Research Program of China (973 Program) (No.2015CB358700), National Natural Science Foundation of China (No.61472206,61073071)
About author:: (TAN Yunzhi， born in 1991， master student. His research interests include machine learning， personalized recommendation and sentiment analysis. ) (ZHANG Min(Corresponding author)， born in 1977， Ph.D.， associate professor. Her research interests include information retrieval and mining， user behavior analysis， machine learning and recommendation systems.) (LIU Yiqun， born in 1981， Ph.D.， associate professor. His research interests include web search technology， information retrieval and user behavior analysis.) (MA Shaoping， born in 1961， Ph.D.， professor. His research interests include intelligent information processing， information retrieval and models and methods of text information retrieval.)
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2016, Vol. 29 
