Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models’ accuracy and ignore issues related to security and the users’ privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users’ private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research.

推荐系统已广泛应用于不同的应用领域，包括节能、电子商务、医疗保健、社交媒体等。此类应用需要分析和挖掘大量各类用户数据，包括人口统计、偏好、社交互动等，以开发准确和精确的推荐系统。此类数据集通常包含敏感信息，但大多数推荐系统都专注于模型的准确性，而忽略了与安全性和用户隐私相关的问题。尽管努力使用不同的风险降低技术来克服这些问题，但在确保密码安全和保护用户私人信息方面没有一个是完全成功的。为了弥合这一差距，区块链技术被认为是一种在推荐系统中促进安全和隐私保护的有前途的策略，不仅因为其安全和隐私的显着特征，还因为其弹性、适应性、容错性和信任特性。本文对基于区块链的推荐系统进行了全面回顾，涵盖挑战、开放问题和解决方案。因此，在指出未来研究的机会之前，引入了一个精心设计的分类法来描述安全和隐私挑战，概述现有框架并讨论它们在使用区块链时的应用和好处。容错和信任特性。本文对基于区块链的推荐系统进行了全面回顾，涵盖挑战、开放问题和解决方案。因此，在指出未来研究的机会之前，引入了一个精心设计的分类法来描述安全和隐私挑战，概述现有框架并讨论它们在使用区块链时的应用和好处。容错和信任特性。本文对基于区块链的推荐系统进行了全面回顾，涵盖挑战、开放问题和解决方案。因此，在指出未来研究的机会之前，引入了一个精心设计的分类法来描述安全和隐私挑战，概述现有框架并讨论它们在使用区块链时的应用和好处。