ABSTRACT
In this paper, we propose a framework that enables the detection of noise in recommender system databases. We consider two classes of noise: natural and malicious noise. The issue of natural noise arises from imperfect user behaviour (e.g. erroneous/careless preference selection) and the various rating collection processes that are employed. Malicious noise concerns the deliberate attempt to bias system output in some particular manner. We argue that both classes of noise are important and can adversely effect recommendation performance. Our objective is to devise techniques that enable system administrators to identify and remove from the recommendation process any such noise that is present in the data. We provide an empirical evaluation of our approach and demonstrate that it is successful with respect to key performance indicators.
- P. Avesani, P. Massa, and R. Tiella. Moleskiing: A trust-aware decentralised recommender system. In Proceedings of the 1st Workshop on Friend of a Friend, Social Networking and the Semantic Web, September 1-2 2004.Google Scholar
- J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pages 43--52, July 24-26 1998. Google ScholarDigital Library
- R. Burke, B. Mobasher, and R. Bhaumik. Limited knowledge shilling attacks in collaborative filtering systems. In Proceedings of Workshop on Intelligent Techniques for Web Personalization (ITWP'05), August 2005.Google Scholar
- J. Canny. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 238--245, August 11-15 2002. Google ScholarDigital Library
- J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230--237, August 15-19 1999. Google ScholarDigital Library
- S. K. Lam and J. Riedl. Shilling recommender systems for fun and profit. In Proceedings of the 13th International World Wide Web Conference, pages 393--402, May 17-20 2004. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust-aware collaborative filtering for recommender systems. In Proceedings of the International Conference on Cooperative Information Systems (CoopIS'04), pages 492--508, October 25-29 2004.Google ScholarCross Ref
- B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Effective attack models for shilling item-based collaborative filtering system. In Proceedings of the 2005 WebKDD Workshop (KDD'2005)I, August 2005.Google Scholar
- M. Montaner, B. Lopez, and J. L. de la Rosa. Developing trust in recommender agents. In Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems, pages 304--305, July 15-19 2002. Google ScholarDigital Library
- J. O'Donovan and B. Smyth. Trust in recommender systems. In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI'05), pages 167--174, January 10-13 2005. Google ScholarDigital Library
- M. P. O'Mahony. Towards Robust and Efficient Automated Collaborative Filtering. PhD thesis, University College Dublin, Department of Computer Science, Belfield, Dublin 4, Ireland, Dec 2004.Google Scholar
- M. P. O'Mahony, N. J. Hurley, and G. C. M. Silvestre. An evaluation of the performance of collaborative filtering. In Proceedings of the 14th Irish International Conference on Artificial Intelligence and Cognitive Science (AICS'03), pages 164--168, September 17-19 2003.Google Scholar
- M. P. O'Mahony, N. J. Hurley, and G. C. M. Silvestre. Efficient and secure collaborative filtering through intelligent neighbour selection. In Proceedings of the 16th European Conference on Artificial Intelligence (ECAI'04), pages 383--387, August 23-27 2004.Google ScholarDigital Library
- M. P. O'Mahony, N. J. Hurley, and G. C. M. Silvestre. Recommender systems: Attack types and strategies. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI-05), pages 334--339, July 9-13 2005. Google ScholarDigital Library
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J.Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW'94), pages 175--186, October 22-26 1994. Google ScholarDigital Library
- P. Resnick and H. R. Varian. Recommender systems -- introduction to the special section. Communications of the ACM, 40(3):56--58, 1997. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the Fifth International Conference on Computer and Information Technology (ICCIT'02), December 27-28 2002.Google Scholar
- J. B. Schafer, J. Konstan, and J. Riedl. Recommender systems in e-commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce, pages 158--166, November 3-5 1999. Google ScholarDigital Library
Index Terms
- Detecting noise in recommender system databases
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