大数据 ›› 2022, Vol. 8 ›› Issue (3): 87-102.doi: 10.11959/j.issn.2096-0271.2022031

• 研究 • 上一篇    

区块链在个性化推荐系统中的应用研究综述

许小颖, 陈熙, 陈源, 谢永靖   

  1. 华南理工大学工商管理学院,广东 广州 510640
  • 出版日期:2022-05-01 发布日期:2022-05-01
  • 作者简介: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。
  • 基金资助:
    国家自然科学基金资助项目(72071083);广东省自然科学基金资助项目(2021A1515012003);广州市哲学社科规划2021年度课题(2021GZQN09)

A review of blockchain applications in personalized recommender systems

Xiaoying XU, Xi CHEN, Yuan CHEN, Yongjing XIE   

  1. School of Business Administration, South China University of Technology, Guangzhou 510640, China
  • Online:2022-05-01 Published:2022-05-01
  • Supported by:
    The National Natural Science Foundation of China(72071083);The Guangdong Natural Science Foundation(2021A1515012003);Guangzhou Philosophy and Social Science Planning Project 2021(2021GZQN09)

摘要:

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。

关键词: 区块链, 推荐系统, 隐私安全, 透明可信

Abstract:

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.

Key words: blockchain, recommender systems, privacy and security, transparency and reliability

中图分类号: 

No Suggested Reading articles found!