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Robust Collaborative Recommendation

  • Robin Burke
  • Michael P. O’Mahony
  • Neil J. Hurley
Chapter

Abstract

Collaborative recommender systems are vulnerable to malicious users who seek to bias their output, causing them to recommend (or not recommend) particular items. This problem has been an active research topic since 2002. Researchers have found that the most widely-studied memory-based algorithms have significant vulnerabilities to attacks that can be fairly easily mounted. This chapter discusses these findings and the responses that have been investigated, especially detection of attack profiles and the implementation of robust recommendation algorithms.

Keywords

Recommender System Target Item Recommendation Algorithm Attack Model Probabilistic Latent Semantic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

Neil Hurley would like to acknowledge the support of Science Foundation Ireland, grant number 08/SRC/I1407: Clique: Graph and Network Analysis Cluster. Michael O’Mahony is supported by Science Foundation Ireland under grant 07/CE/I1147: CLARITY: Centre for Sensor Web Technologies.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Robin Burke
    • 1
  • Michael P. O’Mahony
    • 2
  • Neil J. Hurley
    • 3
  1. 1.Center for Web Intelligence, School of Computer ScienceTelecommunication and Information Systems, DePaul UniversityChicagoUSA
  2. 2.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinDublinIreland
  3. 3.School of Computer Science and InformaticsUniversity College DublinDublinIreland

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