Abstract
Collaborative recommender systems are vulnerable to malicious users who seek to bias their output, causing them to recommend (or not recommend) particular items. This problem has been an active research topic since 2002. Researchers have found that the most widely-studied memory-based algorithms have significant vulnerabilities to attacks that can be fairly easily mounted. This chapter discusses these findings and the responses that have been investigated, especially detection of attack profiles and the implementation of robust recommendation algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
A.Williams, C., Mobasher, B., Burke, R.: Defending recommender systems: detection of profile injection attacks. Service Oriented Computing and Applications pp. 157–170 (2007)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence pp. 43–52 (1998)
Bryan, K., O’Mahony, M., Cunningham, P.: Unsupervised retrieval of attack profiles in collaborative recommender systems. In: RecSys ’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 155–162. ACM, New York, NY, USA (2008). DOI http://doi.acm.org/10.1145/1454008.1454034
Burke, R., Mobasher, B., Bhaumik, R.: Limited knowledge shilling attacks in collaborative filtering systems. In Proceedings of Workshop on Intelligent Techniques for Web Personalization (ITWP’05) (2005)
Burke, R., Mobasher, B., Williams, C.: Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, pp. 17–20 (2006)
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 (2005)
Chirita, P.A., Nejdl, W., Zamfir, C.: Preventing shilling attacks in online recommender systems. In Proceedings of the ACM Workshop on Web Information and Data Management (WIDM’2005) pp. 67–74 (2005)
Dellarocas, C.: Immunizing on–line reputation reporting systems against unfair ratings and discriminatory behavior. In Proceedings of the 2nd ACM Conference on Electronic Commerce (EC’00) pp. 150–157 (2000)
Fug-uo, Z., Sheng-hua, X.: Analysis of trust-based e-commerce recommender systems under recommendation attacks. In: ISDPE ’07: Proceedings of the The First International Symposium on Data, Privacy, and E-Commerce, pp. 385–390. IEEE Computer Society,Washington, DC, USA (2007). DOI http://dx.doi.org/10.1109/ISDPE.2007.55
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval pp. 230–237 (1999)
Hofmann, T.: Collaborative filtering via gaussian probabilistic latent semantic analysis. In: SIGIR ’03: Proceedings of the 26th annual international ACM SIGIR conference on Research 834 Robin Burke, Michael P. O’Mahony and Neil J. Hurley and development in informaion retrieval, pp. 259–266. ACM, New York, NY, USA (2003). DOI http://doi.acm.org/10.1145/860435.860483
Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In Proceedings of the 13th International World Wide Web Conference pp. 393–402 (2004)
Macnaughton-Smith, P., Williams, W.T., Dale, M., Mockett, L.: Dissimilarity analysis – a new technique of hierarchical sub–division. Nature 202, 1034–1035 (1964)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 17–24. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1297231.1297235
Mehta, B., Hofmann, T.: A survey of attack-resistant collaborative filtering algorithms. Bulletin of the Technical Committee on Data Engineering 31(2), 14–22 (2008). URL http://sites.computer.org/debull/A08June/mehta.pdf
Mehta, B., Hofmann, T., Fankhauser, P.: Lies and propaganda: Detecting spam users in collaborative filtering. In: Proceedings of the 12th international conference on Intelligent user interfaces, pp. 14–21 (2007)
Mehta, B., Hofmann, T., Nejdl, W.: Robust collaborative filtering. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 49–56. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1297231.1297240
Mehta, B., Nejdl, W.: Unsupervised strategies for shilling detection and robust collaborative filtering. User Modeling and User-Adapted Interaction 19(1-2), 65–97 (2009). DOI http: //dx.doi.org/10.1007/s11257-008-9050-4
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Effective attack models for shilling item-based collaborative filtering system. In Proceedings of the 2005 WebKDD Workshop (KDD’2005) (2005)
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology 7(4) (2007)
Mobasher, B., Burke, R.D., Sandvig, J.J.: Model-based collaborative filtering as a defense against profile injection attacks. In: AAAI. AAAI Press (2006)
O’Donovan, J., Smyth, B.: Is trust robust?: an analysis of trust-based recommendation. In: IUI ’06: Proceedings of the 11th international conference on Intelligent user interfaces, pp. 101– 108. ACM, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1111449.1111476
O’Mahony, M.P., Hurley, N.J., Silvestre, C.C.M.: An evaluation of neighbourhood formation on the performance of collaborative filtering. Artificial Intelligence Review 21(1), 215–228 (2004)
O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M.: Promoting recommendations: An attack on collaborative filtering. In: A. Hameurlain, R. Cicchetti, R. Traunmüller (eds.) DEXA, Lecture Notes in Computer Science, vol. 2453, pp. 494–503. Springer (2002)
O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M.: An evaluation of the performance of collaborative filtering. In Proceedings of the 14th Irish International Conference on Artificial Intelligence and Cognitive Science (AICS’03) pp. 164–168 (2003)
O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M.: Recommender systems: Attack types and strategies. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI- 05) pp. 334–339 (2005)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., J.Riedl: Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW’94) pp. 175–186 (1994)
Resnick, P., Sami, R.: The influence limiter: provably manipulation-resistant recommender systems. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 25–32. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/ 1297231.1297236
Resnick, P., Sami, R.: The information cost of manipulation-resistance in recommender systems. In: RecSys ’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 147–154. ACM, New York, NY, USA (2008). DOI http://doi.acm.org/10.1145/1454008. 1454033
Rokach, L.: Mining manufacturing data using genetic algorithm-based feature set decomposition, Int. J. Intelligent Systems Technologies and Applications, 4(1):57-78 (2008).
Sandvig, J.J., Mobasher, B., Burke, R.: Robustness of collaborative recommendation based on association rule mining. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pp. 105–112. ACM, New York, NY, USA (2007). DOI http://doi. acm.org/10.1145/1297231.1297249
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item–based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference pp. 285–295 (2001)
Su, X.F., Zeng, H.J., Chen, Z.: Finding group shilling in recommendation system. In: WWW ’05: Special interest tracks and posters of the 14th international conference on World Wide Web, pp. 960–961. ACM, New York, NY, USA (2005). DOI http://doi.acm.org/10.1145/ 1062745.1062818
Williams, C., Mobasher, B., Burke, R., Bhaumik, R., Sandvig, J.: Detection of obfuscated attacks in collaborative recommender systems. In Proceedings of the 17th European Conference on Artificial Intelligence (ECAI’06) (2006)
Yan, X., Roy, B.V.: Manipulation-resistnat collaborative filtering systems. In: RecSys ’09: Proceedings of the 2009 ACM conference on Recommender systems. ACM, New York, NY, USA (2009)
Acknowledgements
Neil Hurley would like to acknowledge the support of Science Foundation Ireland, grant number 08/SRC/I1407: Clique: Graph and Network Analysis Cluster. Michael O’Mahony is supported by Science Foundation Ireland under grant 07/CE/I1147: CLARITY: Centre for Sensor Web Technologies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Burke, R., O’Mahony, M.P., Hurley, N.J. (2011). Robust Collaborative Recommendation. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_25
Download citation
DOI: https://doi.org/10.1007/978-0-387-85820-3_25
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-85819-7
Online ISBN: 978-0-387-85820-3
eBook Packages: Computer ScienceComputer Science (R0)