区块链在个性化推荐系统中的应用研究综述

doi:10.11959/j.issn.2096-0271.2022031

Abstract

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

CLC Number:

Xiaoying XU, Xi CHEN, Yuan CHEN, Yongjing XIE. A review of blockchain applications in personalized recommender systems[J]. Big Data Research, 2022, 8(3): 87-102.

Figures/Tables 4

References 51

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主要解决问题	代表文献	区块链的应用方式
数据安全和隐私保护	文献[10,27-35]	· 区块链使用户的数据信息存储在多个节点上，避免恶意攻击；
		· 用户拥有数据的完全控制权，可对敏感数据加密上链，仅授权方可解密，并且随时可终止授权；
		·区块链上的用户通过账户地址参与交易，无法与真实身份对应；
		·每笔交易信息都记录在区块链上，保护数据不被滥用
数据共享	文献[36-39]	· 区块链上的各节点可自由交易，无须中介机构，通过加密技术对交易细节加密，结合智能合约保证交易高效安全完成；
		· 通过区块链上的智能合约预设规则，对共享数据的用户进行奖励分配
数据可信	文献[30,40-47]	·通过区块链共识机制对用户提交的数据进行验证和记录；
		· 由于共识机制的作用，数据一旦通过验证并写入区块链后就难以篡改；
		· 通过区块链上的智能合约预设规则，对提供有效数据的用户和矿工给予代币激励
推荐透明度	文献[27,48-50]	· 用户、物品、用户评分等都存储在区块链上，计算物品分数的算法存储在智能合约中，用户可选择特定算法来计算物品分数；
		· 区块链与推荐系统深度融合，构建难以篡改的模型，跟踪推荐过程

作者	推荐性能	安全性能	效率性能
杨立等人^[29]	推荐转化率	隐私性、透明性、可验证性、可信性	存储可扩展性（定量）
		（定性）
赵子军等人^[28]	—	安全性（定性）	通信开销、计算开销（定量）
Casino F等人^[31]	预测准确度	隐私性	局部敏感哈希（locality sensitive hashing，LSH）方法的效率（定量）
Li X L等人^[30]	预测准确度	隐私性、安全性（定性）	—
Bosri R等人^[32]	—	隐私性、完整性、可验证性（定性）	计算开销（推荐生成时间，定量）
Lisi A等人^[48]	—	—	吞吐量、燃料消耗、可扩展性（定量）
Wang S等人^[44]	推荐有效性（案例研究、	—	—
	问卷调查）
Yan B W等人^[37]	预测准确度	隐私性、难以篡改（定性）	燃料消耗
董学文等人^[40]	预测准确度	—	吞吐量（定量）
Yeh T Y等人^[49]	预测准确度	—	燃料消耗
Lisi A等人^[38]	—	安全性（定性）	燃料消耗、吞吐量、计算延迟时间（定量）
Sridevi S等人^[46]	预测准确度	—	—
Yan B W等人^[39]	预测准确度	难以篡改、完整性、避免单点故障（定性）	内存消耗、吞吐量、计算延迟时间（定量）
Lin L J等人^[33]	预测准确度	隐私性	—
Cai W H等人^[45]	预测准确度	—	—

A review of blockchain applications in personalized recommender systems

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