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

Author

Listed:
  • Yue Ma

    (Tsinghua University)

  • Guoqing Chen

    (Tsinghua University)

  • Qiang Wei

    (Tsinghua University)

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.

Suggested Citation

  • Yue Ma & Guoqing Chen & Qiang Wei, 2017. "Finding users preferences from large-scale online reviews for personalized recommendation," Electronic Commerce Research, Springer, vol. 17(1), pages 3-29, March.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:1:d:10.1007_s10660-016-9240-9
    DOI: 10.1007/s10660-016-9240-9
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    References listed on IDEAS

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    1. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
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    3. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    4. Decker, Reinhold & Trusov, Michael, 2010. "Estimating aggregate consumer preferences from online product reviews," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 293-307.
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    Cited by:

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    3. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
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    6. Nan Jing & Tao Jiang & Juan Du & Vijayan Sugumaran, 2018. "Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website," Electronic Commerce Research, Springer, vol. 18(1), pages 159-179, March.
    7. Christopher Gerling & Stefan Lessmann, 2024. "Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis," Papers 2411.14463, arXiv.org.
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    10. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.

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