Is trust robust?: an analysis of trust-based recommendation

ABSTRACT

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.

REFERENCES

• 1: P. Avesani, P. Massa, and R. Tiella. Moleskiing: a trust-aware decentralized recommender system. 1st Workshop on Friend of a Friend, Social Networking and the Semantic Web. Galway, Ireland, 2004.
• 2: R. Burke, B. Mobasher, and R. Bhaumik. Limited knowledge shilling attacks in collaborative filtering systems. In Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05, Edinburgh.
• 3: R. Burke, B. Mobasher, R. Zabicki, and R. Bhaumik. Identifying attack models for secure recommendation. In Beyond Personalisation Workshop at the International Conferece on Intelligent User Interfaces, pages 347--361, San Deigo, USA., 2005. ACM Press.
• 4: J. Golbeck and J. Hendler. Accuracy of metrics for inferring trust and reputation in semantic web-based social networks. In Proceedings of EKAW'04, pages LNAI 2416, p. 278 ff., 2004.
• 5: Shyong K. Lam , John Riedl, Shilling recommender systems for fun and profit, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988726]
• 6: P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. Proceedings of International Conference on Cooperative Information Systems, Agia Napa, Cyprus, 2004.
• 7: P. Massa and B. Bhattacharjee. Using trust in recommender systems: an experimental analysis. Proceedings of 2nd International Conference on Trust Managment, Oxford, England, pages 221--235, 2004.
• 8: B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Effictive attack models for shilling item-based collaborative filtering systems. In WebKDD,, Chicago, Illinois, USA., 2005. ACM Press.
• 9: J. O'Donovan and B. Smyth. Eliciting trust values from recommendation errors. In Proceedings of the 18th International FLAIRS Conference, pages 289--294. AAAI Press, 2005.
• 10: John O'Donovan , Barry Smyth, Trust in recommender systems, Proceedings of the 10th international conference on Intelligent user interfaces, January 10-13, 2005, San Diego, California, USA [doi>10.1145/1040830.1040870]
• 11: Michael O'Mahony , Neil Hurley , Nicholas Kushmerick , Guénolé Silvestre, Collaborative recommendation: A robustness analysis, ACM Transactions on Internet Technology (TOIT), v.4 n.4, p.344-377, November 2004 [doi>10.1145/1031114.1031116]
• 12: Michael P. O'Mahony , Neil Hurley , Guenole C. M. Silvestre, Promoting Recommendations: An Attack on Collaborative Filtering, Proceedings of the 13th International Conference on Database and Expert Systems Applications, p.494-503, September 02-06, 2002
• 13: Derry O' Sullivan , David Wilson , Barry Smyth, Improving Case-Based Recommendation: A Collaborative Filtering Approach, Proceedings of the 6th European Conference on Advances in Case-Based Reasoning, p.278-291, September 04-07, 2002
• 14: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 15: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]
• 16: Barry Smyth , Paul McClave, Similarity vs. Diversity, Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development, p.347-361, August 02, 2001

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Human Factors, Reliability

Keywords:
collaborative filtering, recommender systems, robustness, trust