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

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Abstract

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.

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Acknowledgments

The work was partly supported by the National Natural Science Foundation of China (71110107027/71490724/71372044), the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities of China (12JJD630001), and China Retail Research Center of Tsinghua University School of Economics and Management.

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Authors and Affiliations

  1. Research Center for Contemporary Management, Key Research Institute of Humanities and Social Sciences at Universities, School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Yue Ma, Guoqing Chen & Qiang Wei

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  1. Yue Ma
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  2. Guoqing Chen
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  3. Qiang Wei
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Correspondence to Qiang Wei.

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Ma, Y., Chen, G. & Wei, Q. Finding users preferences from large-scale online reviews for personalized recommendation. Electron Commer Res 17, 3–29 (2017). https://doi.org/10.1007/s10660-016-9240-9

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