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Recommender Systems Handbook pp 961–995Cite as

Robust Collaborative Recommendation

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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.

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Notes

  1. 1.

    It could be argued that even such a technique did exist, it would not be in the interest of a collaborative system to deploy it.

  2. 2.

    See [32] for a discussion on informed attacks in cases where alternative similarity metrics are employed. Note that none of the metrics considered provided robustness against attack.

  3. 3.

    Note that an optimal push attack strategy is also presented in [25]. In this case, it is concluded that maximising the correlation between authentic and attack profiles is the primary objective. While this conclusion makes sense, it is important to select attack profile ratings that also maximise prediction shift, as is the case with the popular attack described here.

  4. 4.

    http://www.cs.umn.edu/research/GroupLens/data/.

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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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Authors and Affiliations

  1. Center for Web Intelligence, School of Computer Science, Telecommunication and Information Systems, DePaul University, Chicago, IL, USA

    Robin Burke

  2. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Michael P. O’Mahony & Neil J. Hurley

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  1. Robin Burke
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Correspondence to Robin Burke .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Burke, R., O’Mahony, M.P., Hurley, N.J. (2015). Robust Collaborative Recommendation. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_28

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