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Personalized Recommendation Algorithm Based on Product Reviews

Author

Listed:
  • Zhibo Wang

    (International School of Software, Wuhan University, Wuhan, China)

  • Mengyuan Wan

    (International School of Software, Wuhan University, Wuhan, China)

  • Xiaohui Cui

    (International School of Software, Wuhan University, Wuhan, China)

  • Lin Liu

    (School of Software, East China University of Technology, Shanghai, China)

  • Zixin Liu

    (School of Software, East China University of Technology, Shanghai, China)

  • Wei Xu

    (School of Software, East China University of Technology, Shanghai, China)

  • Linlin He

    (School of Software, East China University of Technology, Shanghai, China)

Abstract

Under the background of leap-forward development for the internet, e-commerce has played an important role in people's daily life, but huge data sizes have also brought problems, such as information overload which can be solved by using a recommendation system effectively. However, with the development of the e-commerce, the amount of the product catalogs and users becomes larger, which causes lower performance of the traditional recommendation system. This article comes up with a personalized recommendation algorithm based on the data mining of product reviews to optimize the performance of the new recommendation system. Features of the product were extracted, for which the users' sentiment polarity was analyzed. This article develops a recommendation system based on the user's preference model and the product features to get the recommendation result. Experimental results show that a personalized recommendation has significantly improved the accuracy and recall rate when compared with a traditional recommendation algorithm.

Suggested Citation

  • Zhibo Wang & Mengyuan Wan & Xiaohui Cui & Lin Liu & Zixin Liu & Wei Xu & Linlin He, 2018. "Personalized Recommendation Algorithm Based on Product Reviews," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 16(3), pages 22-38, July.
    • Handle: RePEc:igg:jeco00:v:16:y:2018:i:3:p:22-38
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    Cited by:

    1. Jianshan Sun & Jian Song & Yuanchun Jiang & Yezheng Liu & Jun Li, 2022. "Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 101-121, March.

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