[ "许小颖(1987- ),男,博士,华南理工大学工商管理学院副教授,主要研究方向为推荐系统、机器学习、大数据分析和区块链应用。" ]
[ "陈熙(1996- ),女,华南理工大学工商管理学院硕士生,主要研究方向为推荐系统、区块链应用。" ]
[ "陈源(1998- ),男,华南理工大学工商管理学院硕士生,主要研究方向为推荐系统、区块链应用。" ]
[ "谢永靖(1993- ),男,华南理工大学工商管理学院硕士生,主要研究方向为管理信息系统、区块链应用。" ]
网络首发:2022-05,
纸质出版:2022-05-15
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许小颖, 陈熙, 陈源, 等. 区块链在个性化推荐系统中的应用研究综述[J]. 大数据, 2022,8(3):87-102.
Xiaoying XU, Xi CHEN, Yuan CHEN, et al. A review of blockchain applications in personalized recommender systems[J]. Big data research, 2022, 8(3): 87-102.
许小颖, 陈熙, 陈源, 等. 区块链在个性化推荐系统中的应用研究综述[J]. 大数据, 2022,8(3):87-102. DOI: 10.11959/j.issn.2096-0271.2022031.
Xiaoying XU, Xi CHEN, Yuan CHEN, et al. A review of blockchain applications in personalized recommender systems[J]. Big data research, 2022, 8(3): 87-102. DOI: 10.11959/j.issn.2096-0271.2022031.
区块链作为一种新兴技术,以其去中心化、难以篡改、匿名性和可追溯性等特点,为个性化推荐系统的改进提供了一种崭新的思路。为此,首先对近年来推荐系统面临的主要问题和区块链技术带来的机遇进行归纳总结,然后采用文献分析方法,从时间分布、文献类型、研究问题和评估指标4个层面,对推荐系统中区块链技术的应用研究进行分析和总结。分析结果表明:区块链对于解决推荐系统的数据安全和隐私保护、数据共享、数据可信和推荐透明度问题有重要意义;已有研究主要集中于解决推荐系统中用户的数据安全和隐私保护问题,而在跨平台数据共享、数据激励机制设计和系统可扩展性等方面的研究仍有待进一步突破。
Blockchain
as an emerging technology
provides a brand-new idea for the improvement of personalized recommender systems with its characteristics of decentralization
tamper-proof
anonymity and traceability.Therefore
the main problems faced by the recommender systems in recent years and the opportunities brought by blockchain technology were summarized firstly.Then
literature analysis was adopted to analyze and summarize the research on the application of blockchain technology in recommender systems from four aspects: time distribution
literature types
research questions and evaluation indicators.The results show that the blockchain is of great significance for solving the problems of data security and privacy protection
data sharing
data trustworthiness and recommendation transparency of recommender systems.Existing studies mainly focus on solving the problem of data security and privacy protection in recommender systems
while further breakthroughs are needed in cross-platform data sharing
design of incentive mechanisms and system scalability.
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