ABSTRACT
Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94), Santiago, Chile, September 1994. Google ScholarDigital Library
- A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of Royal Statistical Society, B(39):1--38, 1977.Google Scholar
- J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval (SIGIR'99), Berkeley, CA, August 1999. Google ScholarDigital Library
- T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 1999. Google ScholarDigital Library
- T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning Journal, 42(1):177--196, 2001. Google ScholarDigital Library
- J. Herlocker, J. Konstan, L. G. Tervin, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5--53, 2004. Google ScholarDigital Library
- X. Jin, Y. Zhou, and B. Mobasher. A unified approach to personalization based on probabilistic latent semantic models of web usage and content. In Proceedings of the AAAI 2004 Workshop on Semantic Web Personalization (SWP'04), San Jose, California, July 2004.Google Scholar
- X. Jin, Y. Zhou, and B. Mobasher. Web usage mining based on probabilistic latent semantic analysis. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'04), Seattle, Washington, August 2004. Google ScholarDigital Library
- S. Lam and J. Riedl. Shilling recommender systems for fun and profit. In Proceedings of the 13th International WWW Conference, New York, May 2004. Google ScholarDigital Library
- B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 7(4), 2007. Google ScholarDigital Library
- B. Mobasher, R. Burke, and J. Sandvig. Model-based collaborative filtering as a defense against profile injection attacks. In Proceedings of the 21st National Conference on Artificial Intelligence, pages 1388--1393. AAAI, July 2006. Google ScholarDigital Library
- M. Nakagawa and B. Mobasher. A hybrid web personalization model based on site connectivity. In WebKDD Workshop at the ACM SIGKKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, August 2003.Google Scholar
- M. O'Conner and J. Herlocker. Clustering items for collaborative filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, August 1999.Google Scholar
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference, Hong Kong, May 2001. Google ScholarDigital Library
- C. Williams, R. Bhaumik, R. Burke, and B. Mobasher. The impact of attack profile classification on the robustness of collaborative recommendation. In Proceedings of the 2006 WebKDD Workshop, held at ACM SIGKDD Conference on Data Mining and Knowledge Discovery (KDD'06), Philadelphia, August 2006.Google Scholar
Index Terms
- Robustness of collaborative recommendation based on association rule mining
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