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A Large-Scale Reviews-Driven Multi-Criteria Product Ranking Approach Based on User Credibility and Division Mechanism

Author

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  • Wenzhi Cao

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

  • Xingen Yang

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

  • Yi Yang

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

Abstract

Massive online reviews provide consumers with the convenience of obtaining product information, but it is still worth exploring how to provide consumers with useful and reliable product rankings. The existing ranking methods do not fully mine user information, rating, and text comment information to obtain scientific and reasonable information aggregation methods. Therefore, this study constructs a user credibility model and proposes a large-scale user information aggregation method to obtain a new product ranking method. First, in order to obtain the aggregate weight of large-scale users, this paper proposes a consistency modeling method of text comments and star ratings by mining the associated information of user comments, including user interaction information and user personalized characteristics information, combined with sentiment analysis technology, and then constructs a user credibility model. Second, a double-layer group division mechanism considering user regions and comment time is designed to develop the large-scale group ratings aggregation approach. Third, based on the user credibility model and the large-scale ratings aggregation approach, a product ranking method is developed. Finally, the feasibility and effectiveness of the proposed method are verified through a case study for automobile ranking and a comparative analysis is furnished. The analysis results of the application case of automobile ranking show that there is a significant difference between the ranking results obtained by the ratings aggregation method based on the arithmetic mean and the ranking results obtained by this method. The method in this study comprehensively considers user credibility and group division, which can be reflected in user aggregation weights and the group aggregation process, and can also obtain more scientific and reasonable decision results.

Suggested Citation

  • Wenzhi Cao & Xingen Yang & Yi Yang, 2023. "A Large-Scale Reviews-Driven Multi-Criteria Product Ranking Approach Based on User Credibility and Division Mechanism," Mathematics, MDPI, vol. 11(13), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2952-:d:1185137
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    References listed on IDEAS

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    1. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    2. Amal Almansour & Reem Alotaibi & Hajar Alharbi, 2022. "Text-rating review discrepancy (TRRD): an integrative review and implications for research," Future Business Journal, Springer, vol. 8(1), pages 1-15, December.
    3. Bidyut Hazarika & Kuanchin Chen & Muhammad Razi, 2021. "Are numeric ratings true representations of reviews? A study of inconsistency between reviews and ratings," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 38(1), pages 85-106.
    4. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
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    Cited by:

    1. Minhui Ren & Yu Fan & Jindong Chen & Jian Zhang, 2023. "A Multi-Stage Model for Perceived Quality Evaluation of Clothing Brands," Mathematics, MDPI, vol. 11(18), pages 1-16, September.

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