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Enhancing a Business Recommendation System: Leveraging Blockchain Technology with a Differentiated Scoring Incentive Mechanism

Author

Listed:
  • Zhijian Lan

    (City University of Macau)

  • Shuyue Li

    (City University of Macau)

  • Jinsheng Li

    (Guilin University of Technology)

  • Liang Chen

    (Guilin University of Technology)

Abstract

Recommender systems play a pivotal role in offering personalized suggestions for products, items, and services within the commercial sector. This capability has significantly boosted profits for various platforms. Yet, a notable challenge within these systems remains the sparsity of user ratings. This paper introduces a novel approach to tackle the challenge of sparsity in user ratings within recommender systems, proposing a business recommendation system leveraging blockchain technology. The core innovation lies in a rating incentive system designed to address the scarcity of user ratings in commercial recommender systems. The revamped rating incentive system diverges from conventional undifferentiated scoring approaches. Instead, it introduces a differentiated scoring incentive mechanism based on user contributions. This strategy aims to motivate users to provide high-quality ratings, thereby enhancing the reliability and richness of the rating pool. To mitigate trust risks associated with these differentiated incentives, the integration of blockchain technology into the web-based business platform ensures transparency and fosters trust among users. Simulation experiments conducted on the Epinion dataset validate the effectiveness of this mechanism. The mean value of the differentiated scoring incentive mechanism stabilizes at 8.5, showcasing a marked difference from the non-differentiated incentive mechanism. These experimental results underscore the suitability of this scoring mechanism for business platforms with significant data flow. Moreover, it effectively bolsters user ratings within recommendation systems, subsequently augmenting the enterprise’s revenue on the platform.

Suggested Citation

  • Zhijian Lan & Shuyue Li & Jinsheng Li & Liang Chen, 2024. "Enhancing a Business Recommendation System: Leveraging Blockchain Technology with a Differentiated Scoring Incentive Mechanism," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 19051-19070, December.
  • Handle: RePEc:spr:jknowl:v:15:y:2024:i:4:d:10.1007_s13132-024-01812-4
    DOI: 10.1007/s13132-024-01812-4
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    References listed on IDEAS

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    1. Paul Resnick & Christopher Avery & Richard Zeckhauser, 1999. "The Market for Evaluations," American Economic Review, American Economic Association, vol. 89(3), pages 564-584, June.
    2. Youjie Liu & Mohammad Shabaz, 2022. "Design and research of computer network micro-course management system based on JSP technology," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 203-211, March.
    3. Xiaoying Xu & Kaushik Dutta & Anindya Datta & Chunmian Ge, 2018. "Identifying functional aspects from user reviews for functionality†based mobile app recommendation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(2), pages 242-255, February.
    4. Marten Risius & Kai Spohrer, 2017. "A Blockchain Research Framework," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(6), pages 385-409, December.
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