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Artificial Intelligence Review

, Volume 21, Issue 3–4, pp 215–228 | Cite as

An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering

  • M.P. O'mahony
  • N.J. Hurley
  • G.C.M. Silvestre
Article

Abstract

Personalisation features are key to the success of many web applications and collaborative recommender systems have been widely implemented. These systems assist users in finding relevant information or products from the vast quantities that are frequently available. In previous work, we have demonstrated that such systems are vulnerable to attack and that recommendations can be manipulated. We introduced the concept of robustness as a performance measure, which is defined as the ability of a system to provide consistent predictions in the presence of noise in the data. In this paper, we expand on our previous work by examining the effects of several neighbourhood formation schemes and similarity measures on system performance. We propose a neighbourhood filtering mechanism for filtering false profiles from the neighbourhood in order to improve the robustness of the system.

collaborative filtering information retrieval robustness 

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References

  1. Breese, J. S., Heckerman, D. & Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI '98), San Francisco, CA, USA, 43–52.Google Scholar
  2. Dellarocas, C. (2000). Immunizing Online Reputation Reporting Systems Against Unfair Ratings and Discriminatory Behavior. In Proceedings of the 2nd ACM Conference on Electronic Commerce, Minneapolis, MN.Google Scholar
  3. Herlocker, J., Konstan, J., Borchers, A. & Riedl, J. (1999). An Algorithmic Framework for Performing Collaborative Filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, Berkeley, CA, USA. 230–237.Google Scholar
  4. Oard, D. W., Leuski, A. & Stubblebine, S. (2003). Protecting the Privacy of Observable Behavior in Distributed Recommender Systems. In Proceedings of SIGIR'03 Workshop on Implicit Measures of User Interests and Preferences, Toronto, Canada.Google Scholar
  5. O'Mahony, M. P., Hurley, N. J. & Silvestre, G. C. M. (2003). Collaborative Filtering-Safe and Sound? In Proceedings of the 14th International Symposium on Methodologies for Intelligent Systems, Maebashi TERRSA, Maebashi City, Japan.Google Scholar
  6. O'Mahony, M. P., Hurley, N. J. & Silvestre, G. C. M. (2002). Towards Robust Collaborative Filtering. In Proceedings of the 13th Irish International Conference, AICS 2002, Limerick, Ireland, 87–94.Google Scholar
  7. Ramakrishnan, N., Keller, B. J., Mirza, B. J., Grama, A. Y. & Karypis, G. (2001). Privacy Risks in Recommender Systems. In IEEE Internet Computing Vol 5 (6), pp. 54–62.Google Scholar
  8. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. & Riedl, J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, USA, 175–186.Google Scholar
  9. Sarwar, B. M., Karypis, G., Konstan, J. A. & Riedl, J. (2000). Analysis of Recommendation Algorithms for E-commerce. In Proceedings of the 2nd ACM Conference on Electronic Commerce (EC-00), Minneapolis, MN, USA, 158–167.Google Scholar
  10. Shardanand, U., Maes, P. (1995). Social Information Filtering: Algorithms for Automating Word of Mouth. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, Denver, CO, USA, 210–217.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • M.P. O'mahony
  • N.J. Hurley
  • G.C.M. Silvestre

There are no affiliations available

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