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 Filler Item 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

The authors would like to thank the anonymous reviewer for some helpful suggestions. O’Mahony and Hurley are supported by Science Foundation Ireland under Grant Number SFI/12/RC/2289.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Robin Burke
    • 1
  • Michael P. O’Mahony
    • 2
  • Neil J. Hurley
    • 2
  1. 1.Center for Web Intelligence, School of Computer Science, Telecommunication and Information SystemsDePaul UniversityChicagoUSA
  2. 2.Insight Centre for Data AnalyticsUniversity College DublinDublinIreland

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