Abstract
In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naïve Bayesian classifier-based collaborative filtering algorithm is examined. Real data-based experiments are conducted and each attack type’s performance is explicated. Since existing measures, which are used to assess the success of shilling attacks, do not work on binary data, a new evaluation metric is proposed. Empirical outcomes show that it is possible to manipulate binary rating-based recommender systems’ predictions by inserting malicious user profiles. Hence, it is shown that naïve Bayesian classifier-based collaborative filtering scheme is not robust against shilling attacks.
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
Miyahara, K., Pazzani, M.J.: Collaborative filtering with the simple Bayesian classifier. In: The 6th Pacific Rim International Conference on Artificial Intelligence, Melbourne, Australia, pp. 679–689 (2000)
O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M.: Towards robust collaborative filtering. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds.) AICS 2002. LNCS (LNAI), vol. 2464, pp. 87–94. Springer, Heidelberg (2002)
O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M.: Promoting recommendations: An attack on collaborative filtering. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, pp. 494–503. Springer, Heidelberg (2002)
Burke, R.D., Mobasher, B., Bhaumik, R.: Limited knowledge shilling attacks in collaborative filtering systems. In: Workshop on Intelligent Techniques for Web Personalization, Edinburgh, UK (2005)
Burke, R.D., Mobasher, B., Bhaumik, R., Williams, C.A.: Segment-based injection attacks against collaborative filtering recommender systems. In: The 5th IEEE International Conference on Data Mining, Houston, TX, USA, pp. 577–580 (2005)
Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: The 2nd ACM Conference on Electronic Commerce, Minneapolis, MN, USA, pp. 150–157 (2000)
O’Mahony, M.P.: Towards robust and efficient automated collaborative filtering. PhD Dissertation, University College Dublin (2004)
Lam, S.K., Riedl, J.T.: Shilling recommender systems for fun and profit. In: The 13th International Conference on WWW, New York, NY, USA, pp. 393–402 (2004)
Lam, S.K., Riedl, J.T.: Privacy, shilling, and the value of information in recommender systems. In: User Modeling Workshop on Privacy-Enhanced Personalization, Edinburgh, UK, pp. 85–92 (2005)
Mobasher, B., Burke, R.D., Bhaumik, R., Sandvig, J.J.: Attacks and remedies in collaborative recommendation. IEEE Intelligent Systems 22(3), 56–63 (2007)
Mobasher, B., Burke, R.D., Bhaumik, R., Williams, C.A.: Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology 7(4), 23–60 (2007)
Cheng, Z., Hurley, N.J.: Effective diverse and obfuscated attacks on model-based recommender systems. In: The 3rd ACM International Conference on Recommender Systems, New York, NY, USA, pp. 141–148 (2009)
Oostendorp, N., Sami, R.: The copied-item injection attack. In: Workshop on Recommender Systems and the Social Web, New York, NY, USA, pp. 63–70 (2009)
Gunes, I., Kaleli, C., Bilge, A., Polat, H.: Shilling attacks against recommender systems: A comprehensive survey. Artificial Intelligence Review (2012), doi: http://dx.doi.org/10.1007/s10462-012-9364-9
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaleli, C., Polat, H. (2013). Robustness Analysis of Naïve Bayesian Classifier-Based Collaborative Filtering. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-39878-0_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39877-3
Online ISBN: 978-3-642-39878-0
eBook Packages: Computer ScienceComputer Science (R0)