Graph-based detection of shilling attacks in recommender systems

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

25 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
DOIs
StatePublished - Dec 1 2013
Event2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013 - Southampton, United Kingdom
Duration: Sep 22 2013Sep 25 2013

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013
CountryUnited Kingdom
CitySouthampton
Period9/22/139/25/13

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

Keywords

  • Collaborative Filtering
  • Graph
  • Heuristic
  • Largest Component
  • Recommender Systems
  • Robust

Fingerprint Dive into the research topics of 'Graph-based detection of shilling attacks in recommender systems'. Together they form a unique fingerprint.

Cite this