Abstract
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.
This research was supported in part by the National Science Foundation Cyber Trust program under Grant IIS-0430303.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burke, R., Mobasher, B., Zabicki, R., Bhaumik, R.: Identifying attack models for secure recommendation. In: Beyond Personalization: A Workshop on the Next Generation of Recommender Systems, San Diego, California (2005)
Burke, R., Mobasher, B., Bhaumik, R.: Limited knowledge shilling attacks in collaborative filtering systems. In: Proceedings of the 3rd IJCAI Workshop in Intelligent Techniques for Personalization, Edinburgh, Scotland (2005)
Lam, S., Reidl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th International WWW Conference, New York (2004)
O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: Arobustness analysis. ACM Transactions on Internet Technology 4(4), 344–377 (2004)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference, Hong Kong (2001)
Chirita, P.A., Nejdl, W., Zamfir, C.: Preventing shilling attacks in online recommender systems. In: WIDM 2005: Proceedings of the 7th annual ACM international workshop on Web information and data management, pp. 67–74. ACM Press, New York (2005)
Su, X.F., Zeng, H.J., Chen, Z.: Finding group shilling in recommendation system. In: Proceedings of the 14th international World Wide Web Conference, WWW 2005 (2005)
OMahony, M., Hurley, N., Silvestre, G.: Utility-based neighbourhood formation for efficient and robust collaborative filtering. In: Proceedings of the 5th ACM Conference on Electronic Commerce (EC 2004), pp. 260–261 (2004)
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Effective attack models for shilling item-based collaborative filtering systems. In: Proceedings of the 2005 WebKDD Workshop, held in conjuction with ACM SIGKDD 2005, Chicago, Illinois (2005)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic frame work for performing collaborative filtering. In: Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval (SIGIR 1999), Berkeley, CA (1999)
Herlocker, J., Konstan, J., Tervin, L.G., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW 1994: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM Press, New York (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mobasher, B., Burke, R., Williams, C., Bhaumik, R. (2006). Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation. In: Nasraoui, O., Zaïane, O., Spiliopoulou, M., Mobasher, B., Masand, B., Yu, P.S. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2005. Lecture Notes in Computer Science(), vol 4198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11891321_6
Download citation
DOI: https://doi.org/10.1007/11891321_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46346-7
Online ISBN: 978-3-540-46348-1
eBook Packages: Computer ScienceComputer Science (R0)