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A multi-aspect user-interest model based on sentiment analysis and uncertainty theory for recommender systems

Author

Listed:
  • Lihua Sun

    (Tianjin University)

  • Junpeng Guo

    (Tianjin University)

  • Yanlin Zhu

    (Tianjin University)

Abstract

This work presents a new multi-aspect user-interest model for recommender systems to improve recommendation and prediction accuracy. We introduce the overall user satisfaction for a product to build a user-interest profile by computing the user-interest levels from multi-aspect reviews. A domain emotional dictionary is built to overcome the gap in quantity between negative and positive words for sentiment polarity analysis. A sentiment analysis model is designed to characterize the users’ sentiment polarity and strength based on uncertainty theory and the domain emotional dictionary. Accordingly, a new multi-aspect user-interest model is proposed by considering the sentiment analysis model with the user-interest profile. Then, our proposed model is applied to recommender systems and experimentally tested on five products of different categories from three e-commerce websites. Our model not only outperforms the traditional and state-of-the-art models on rating prediction tasks but also improves the recommendation accuracy in multiple domains.

Suggested Citation

  • Lihua Sun & Junpeng Guo & Yanlin Zhu, 2020. "A multi-aspect user-interest model based on sentiment analysis and uncertainty theory for recommender systems," Electronic Commerce Research, Springer, vol. 20(4), pages 857-882, December.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:4:d:10.1007_s10660-018-9319-6
    DOI: 10.1007/s10660-018-9319-6
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    References listed on IDEAS

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    1. 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.
    2. Chen, Xiaowei, 2012. "Variation analysis of uncertain stationary independent increment processes," European Journal of Operational Research, Elsevier, vol. 222(2), pages 312-316.
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    Cited by:

    1. Jin Zhang & Jilong Zhang & Guoqing Chen, 2023. "A semantic transfer approach to keyword suggestion for search engine advertising," Electronic Commerce Research, Springer, vol. 23(2), pages 921-947, June.

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