Detection of shilling attacks in recommender systems via spectral clustering

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations


Collaborative filtering has been widely used in recommender systems as a method to recommend items to users. However, recommender systems utilizing collaborative filtering as their key algorithms are vulnerable to shilling attacks which can generate fake profiles to increase or decrease the popularity of a targeted set of items. In this paper, we present a spectral clustering method to make recommender systems resistant to these attacks in the case that the attack profiles are highly correlated with each other. We formulate the problem as finding a maximum submatrix in the user-user similarity matrix. To search for the maximum submatrix, we translate the matrix into a graph and apply a spectral clustering algorithm to find the min-cut solution to estimate the highly correlated group. The graph is created based on the edge density in order to allow dealing with an unbalanced clustering. The detection is refined through an iterative process to obtain a better estimate of attack profiles. Experimental results show that the proposed approach can improve detection precision compared to existing methods.

Original languageEnglish (US)
Title of host publicationFUSION 2014 - 17th International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788490123553
StatePublished - Jan 1 2014
Event17th International Conference on Information Fusion, FUSION 2014 - Salamanca, Spain
Duration: Jul 7 2014Jul 10 2014


Other17th International Conference on Information Fusion, FUSION 2014

All Science Journal Classification (ASJC) codes

  • Information Systems


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