An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Proceedings of the 10th international conference on Intelligent user interfaces
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
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The open nature of collaborative recommender systems present a security problem. Attackers that cannot be readily distinguished from ordinary users may inject biased profiles, degrading the objectivity and accuracy of the system over time. The standard user-based collaborative filtering algorithm has been shown quite vulnerable to such attacks. In this paper, we examine relevance measures that complement neighbor similarity and their influence on algorithm robustness. In particular, we consider two techniques, significance weighting and trust weighting, that attempt to calculate the utility of a neighbor with respect to rating prediction. Such techniques have been used to improve prediction accuracy in collaborative filtering. We show that significance weighting, in particular, also results in improved robustness under profile injection attacks.