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Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website

Author

Listed:
  • Nan Jing

    (Shanghai University)

  • Tao Jiang

    (Shanghai University)

  • Juan Du

    (Shanghai University
    Shanghai University)

  • Vijayan Sugumaran

    (Oakland University)

Abstract

In the era of electronic commerce, online customer reviews (OCRs) have become a prevalent and valuable information source for both customers and merchants to make business decisions. This paper proposes an enhanced collaborative filtering approach based on sentiment assessment to discover the potential preferences of customers, and to predict customers’ future requirements for business services or products (collectively referred to as “entities”). Specifically, this approach involves three major steps: aspect-level sentiment assessment, customer preference mining and personalized recommendation. First, the aspect-level sentiment assessment transforms OCRs to a structured aspect-level review vector. Second, customer preference mining uses the vector to extract aspect-level feature words from sentiments and assigns polarity score to each sentiment. Finally, the feature words and sentiment polarity score are used to calculate customer preference and customers’ similarities. Personalized recommendation for services and products are generated according to customer similarity. Experiments are conducted based on the data from one of the most popular electronic commerce websites in China ( www.JD.com ). The results demonstrate that the proposed approach outperforms traditional collaborative filtering approaches in effectively recommending entities to target customers especially in the long term.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:elcore:v:18:y:2018:i:1:d:10.1007_s10660-017-9275-6
    DOI: 10.1007/s10660-017-9275-6
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    References listed on IDEAS

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    1. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    2. 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.
    3. 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:

    1. Fan Zou & Yupeng Li & Jiahuan Huang, 2022. "Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign," Electronic Commerce Research, Springer, vol. 22(4), pages 1131-1151, December.
    2. Mozhu Wang & Jianming Yao, 2023. "A reliable location design of unmanned vending machines based on customer satisfaction," Electronic Commerce Research, Springer, vol. 23(1), pages 541-575, March.
    3. Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.
    4. Weiwei Deng & Jian Ma, 2022. "A knowledge graph approach for recommending patents to companies," Electronic Commerce Research, Springer, vol. 22(4), pages 1435-1466, December.
    5. Weiwei Deng, 2022. "Leveraging consumer behaviors for product recommendation: an approach based on heterogeneous network," Electronic Commerce Research, Springer, vol. 22(4), pages 1079-1105, December.
    6. Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
    7. Sarah Bayer & Henner Gimpel & Daniel Rau, 2021. "IoT-commerce - opportunities for customers through an affordance lens," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 27-50, March.
    8. Lifeng He & Dongmei Han & Xiaohang Zhou & Zheng Qu, 2020. "The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model," IJERPH, MDPI, vol. 17(10), pages 1-18, May.

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