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.
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The dataset can be accessed upon request.
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Acknowledgements
The authors would like to thank the Guilin University of Technology for supporting the research in this paper.
Funding
This project is supported by Guilin University of Technology. The project name is “The Impact Mechanism of Personalized Contracts on Employee Creativity: A Perspective Based on Person-Environment Fit and Quasi-Familial Exchange,” Project number: 72262010.
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Data collection and analysis: Zhijian Lan and Shuyue Li.
Conceptualization and research methods: Shuyue Li and Jinsheng Li.
Investigation: Zhijian Lan and Liang Chen.
Writing: Liang Chen and Shuyue Li.
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Lan, Z., Li, S., Li, J. et al. Enhancing a Business Recommendation System: Leveraging Blockchain Technology with a Differentiated Scoring Incentive Mechanism. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01812-4
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DOI: https://doi.org/10.1007/s13132-024-01812-4