GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
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
SuggestBot: using intelligent task routing to help people find work in wikipedia
Proceedings of the 12th international conference on Intelligent user interfaces
Attacks and Remedies in Collaborative Recommendation
IEEE Intelligent Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
PITTCULT: trust-based cultural event recommender
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative web search: a robustness analysis
Artificial Intelligence Review
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Personalizing Trust in Online Auctions
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Manipulation-resistant collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
Impact of relevance measures on the robustness and accuracy of collaborative filtering
EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Analysis of robustness in trust-based recommender systems
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Sequence-based trust in collaborative filtering for document recommendation
International Journal of Human-Computer Studies
A Case Study of Collaboration and Reputation in Social Web Search
ACM Transactions on Intelligent Systems and Technology (TIST)
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
A user trust-based collaborative filtering recommendation algorithm
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
Building and managing reputation in the environment of Chinese e-commerce: a case study on Taobao
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
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Systems that adapt to input from users are susceptible to attacks from those same users. Recommender systems are common targets for such attacks since there are financial, political and many other motivations for influencing the promotion or demotion of recommendable items [2].Recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy and robustness of recommendations. In this paper we examine the effect of using five different trust models in the recommendation process on the robustness of collaborative filtering in an attack situation. In our analysis we also consider the quality and accuracy of recommendations. Our results caution that including trust models in recommendation can either reduce or increase prediction shift for an attacked item depending on the model-building process used, while highlighting approaches that appear to be more robust.