  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.
