Defending Suspected Users by Exploiting Specific Distance Metric in Collaborative Filtering Recommender Systems

Collaborative filtering recommender systems (CFRSs) are critical components of existing popular e-commerce websites to make personalized recommendations. In practice, CFRSs are highly vulnerable to "shilling" attacks or "profile injection" attacks due to its openness. A number of detection methods have been proposed to make CFRSs resistant to such attacks. However, some of them distinguished attackers by using typical similarity metrics, which are difficult to fully defend all attackers and show high computation time, although they can be effective to capture the concerned attackers in some extent. In this paper, we propose an unsupervised method to detect such attacks. Firstly, we filter out more genuine users by using suspected target items as far as possible in order to reduce time consumption. Based on the remained result of the first stage, we employ a new similarity metric to further filter out the remained genuine users, which combines the traditional similarity metric and the linkage information between users to improve the accuracy of similarity of users. Experimental results show that our proposed detection method is superior to benchmarked method.