Shilling Attacks on Recommender System: A Critical Analysis

Because of information overload, Recommender systems help users cope with searching for products and services. To this end Collaborative Filtering (CF) recommender systems have been introduced. However collaborative recommender systems are vulnerable to attacks by their very nature. Attackers can insert fake and biased profiles to undermine the system. This paper provides analysis of shilling attacks on recommender systems. Profile injection attacks are vulnerable to a lot of algorithms. Almost every major e-commerce site is using a recommender system these days, and this brings a lot of challenges. In this paper, we have taken out the findings and limitations after a lot of review and have taken out many major research fields, in which future work can be done.

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