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[Publication] Distributed Collaborative Filtering for Robust Recommendations against Shilling Attacks
2007/05/01 14:32
Ae-Ttie Ji, Cheol Yeon, Heung-Nam Kim , Geun-Sik Jo,
"Distributed Collaborative Filtering for Robust Recommendations against Shilling Attacks",
Lecture Notes in Artificial Intelligence (20th Canadian Conference on Artificial Intelligence),
Vol. 4509, pp. 14-25, Springer-Verlag, May. 2007
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 TCFMA architecture 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 Advogato algorithm. The experimental evaluation on Epinions.com datasets shows that the proposed method brings significant advantages in terms of dealing with profile injection attack without any loss of prediction quality.
발표자료
"Distributed Collaborative Filtering for Robust Recommendations against Shilling Attacks",
Lecture Notes in Artificial Intelligence (20th Canadian Conference on Artificial Intelligence),
Vol. 4509, pp. 14-25, Springer-Verlag, May. 2007
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 TCFMA architecture 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 Advogato algorithm. The experimental evaluation on Epinions.com datasets shows that the proposed method brings significant advantages in terms of dealing with profile injection attack without any loss of prediction quality.
발표자료
Distributed_Collaborative_Filtering_for_Robust_Recommendatio.ppt
