GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Case amazon: ratings and reviews as part of recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Proceedings of the 9th ACM international symposium on Mobility management and wireless access
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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One area of research which has recently gained importance is the security of recommender systems. Malicious users may influence the recommender system by inserting biased data into the system. Such attacks may lead to erosion of user trust in the objectivity and accuracy of the system. In this paper, we propose a new approach for creating attack strategies. Our paper explores the importance of target item and filler items in mounting effective shilling attacks. Unlike previous approaches, we propose strategies built specifically for user based and item based collaborative filtering systems. Our attack strategies are based on intelligent selection of filler items. Filler items are selected on the basis of the target item rating distribution. We show through experiments that our strategies are effective against both user based and item based collaborative filtering systems. Our approach is shown to provide substantial improvement in attack effectiveness over existing attack models.