Distributed Collaborative Filtering for Robust Recommendations Against Shilling Attacks

Authors:
Ae-Ttie Ji;Cheol Yeon;Heung-Nam Kim;Geun-Sik Jo
Affiliations:
Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University,;Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University,;Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University,;School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon,402-751, Korea
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. Collaborative Filtering (CF), one of the most successful technologies in recommender systems suffers from improper use of personal information and the incredibility of recommendations. To deal with these issues, we have been focusing on the trust relationships between individuals, i.e. web of trust, especially for protecting the recommender system against profile injection attack. Based on trust propagation scheme, we proposed TCFMAarchitecture which is added agent-based scheme obtaining attack resistance property as well as improving the efficiency of distributed computing. In web of trust, users' personal agents find a unique migration path made up of latent neighborhoods and reduce search scope to a reasonable level for mobile agents by using the Advogatoalgorithm. The experimental evaluation on Epinions.comdatasets shows that the proposed method brings significant advantages in terms of dealing with profile injection attack without any loss of prediction quality.