@inproceedings{a1d836aebd4e49dbbb893b4b5b5a0fa3,
title = "Graph-based detection of shilling attacks in recommender systems",
abstract = "Collaborative filtering has been widely used in recom-mender systems as a method to recommend items to users. However, by using knowledge of the recommendation algorithm, shilling attackers can generate fake profiles to increase or decrease the popularity of a targeted set of items. In this paper, we present a 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 similarity matrix. We search for the maximum submatrix by transforming the problem into a graph and merging nodes by heuristic functions or finding the largest component. Experimental results show that the proposed approach can improve detection precision compared to state of art methods.",
keywords = "Collaborative Filtering, Graph, Heuristic, Largest Component, Recommender Systems, Robust",
author = "Zhuo Zhang and Kulkarni, {Sanjeev R.}",
year = "2013",
doi = "10.1109/MLSP.2013.6661953",
language = "English (US)",
isbn = "9781479911806",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
booktitle = "2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013",
note = "2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013 ; Conference date: 22-09-2013 Through 25-09-2013",
}