GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
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
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Smart cheaters do prosper: defeating trust and reputation systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Proceedings of the third ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Secure personalized recommendation system for mobile user
ICISC'10 Proceedings of the 13th international conference on Information security and cryptology
The influence of interaction attributes on trust in virtual communities
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
COMPARS: toward an empirical approach for comparing the resilience of reputation systems
Proceedings of the 4th ACM conference on Data and application security and privacy
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Much research has recently been carried out on the incorporation of trust models into recommender systems. It is generally understood that trust-based recommender systems can help to improve the accuracy of predictions. Moreover they provide greater robustness against profile injection attacks by malicious users. In this paper we analyze these contentions in the context of two trust-based algorithms. We note that one of the characteristics of trust-based algorithms is that ratings are often exposed in the user population in order for users to develop opinions on the trustworthiness of their peers. We will argue that exposing ratings presents a robustness vulnerability in these systems and we will show how this vulnerability can be exploited in the development of profile injection attacks. We conclude that the improved accuracy obtained in trust-based systems may well come at a cost of decreased robustness. In the end, trust models should be selected very carefully when building trust-based collaborative filtering (CF) systems.