Finding users preferences from large-scale online reviews for personalized recommendation

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• 作者：Yue Ma ; Guoqing Chen ; Qiang Wei
• 关键词：Online review ; Recommendation systems ; Collaborative filtering ; User preference ; Opinion mining
• 刊名：Electronic Commerce Research
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：17
• 期：1
• 页码：3-29
• 全文大小：
• 刊物主题：IT in Business; Data Structures, Cryptology and Information Theory; Operation Research/Decision Theory; Computer Communication Networks; Business and Management, general; e-Commerce/e-business;
• 出版者：Springer US
• ISSN：1572-9362
• 卷排序：17

文摘

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.