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

Zhijian Lan, Shuyue Li, Jinsheng Li and Liang Chen ()
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Zhijian Lan: City University of Macau
Shuyue Li: City University of Macau
Jinsheng Li: Guilin University of Technology
Liang Chen: Guilin University of Technology

Journal of the Knowledge Economy, 2024, vol. 15, issue 4, No 128, 19070 pages

Abstract: 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.

Keywords: Recommendation systems; Sparsity; Differentiated incentive mechanism; Blockchain technology; User trust; Sustainable engagement (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-024-01812-4

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