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Recommender systems (RSs) effectively curb information overload by providing personalized suggestions of items to users across different online domains. Their widespread use in e-commerce enhances user engagement, personalizes shopping experiences, and drives sales growth. However, despite the effectiveness of these systems at generating recommendations for users, they still raise major privacy and security concerns as their data could be exploited for malicious purposes, which can lead to data breaches and misuse. Therefore, this paper presents a comprehensive and systematic review of the underlying causes of privacy and security challenges in RS. It also provides a detailed taxonomy categorizing these concerns based on their targets and the risks they create. It further presents potential solutions that have been used in the literature while identifying challenges and possible research directions to pursue in a bid to address privacy and security concerns in RSs. This paper will be a useful resource for current and upcoming researchers in the domain of RSs. It will support knowledge advancement and steer appropriate research directions.
Ojokoh et al. (Fri,) studied this question.