›› 2013, Vol. 28 ›› Issue (4): 616-624.doi: 10.1007/s11390-013-1362-0

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section of EDB2012 • Previous Articles     Next Articles

Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

Xiang-Liang Zhang1 (张响亮), Tak Man Desmond Lee1, and Georgios Pitsilis2   

  1. 1. King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;
    2. Faculty of Science, Technology and Communication, University of Luxembourg, Luxembourg
  • Received:2012-09-10 Revised:2013-05-02 Online:2013-07-05 Published:2013-07-05

Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system Epinions.com.

[1] Lam S K, Riedl J. Shilling recommender systems for fun andprofit. In Proc. the 13th International Conference on WorldWide Web (WWW), May 2004, pp.393-402.

[2] Mehta B. Unsupervised shilling detection for collaborative fil-tering. In Proc. the 22nd National Conference on ArtificialIntelligence, Jul. 2007, pp.1402-1407.

[3] Mobasher B, Burke R D, Sandvig J J. Model-based collabora-tive filtering as a defense against profile injection attacks. InProc. the 21st National Conference on Artificial Intelligence,Jul. 2006, pp.1388-1393.

[4] Su X F, Zeng H J, Chen Z. Finding group shilling in recom-mendation system. In Special Interest Tracks and Posters ofthe 14th WWW, May 2005, pp.960-961.

[5] Zhang S, Chakrabarti A, Ford J, Makedon F. Attack detec-tion in time series for recommender systems. In Proc. the12th ACM SIGKDD International Conference on KnowledgeDiscovery and Data Mining, Aug. 2006, pp.809-814.

[6] DuBois T, Golbeck J, Kleint J, Srinivasan A. Improving rec-ommendation accuracy by clustering social networks withtrust. In Proc. ACM Workshop on Recommender Systems &the Social Web, Oct. 2009.

[7] Sarwar B M, Karypis G, Konstan J, Riedl J. Recommendersystems for large-scale e-commerce: Scalable neighborhoodformation using clustering. In Proc. the 5th Int. Conf. Com-puter and Information Technology, Dec. 2002.

[8] Pitsilis G, Zhang X L, Wang W. Clustering recommendersin collaborative filtering using explicit trust information. InProc. the 5th IFIP WG 11.11 International Conference onTrust Management (IFIPTM), Jun. 2011, pp.82-97.

[9] Truong K Q, Ishikawa F, Honiden S. Improving accuracy ofrecommender system by item clustering. Transactions on In-formation and Systems, 2007, E90-D(9): 1363-1373.

[10] O'Connor M, Herlocker J. Clustering items for collaborativefiltering. In Proc. SIGIR 2001 Workshop on RecommenderSystems, Sept. 2001.

[11] O'Donovan J, Smyth B. Trust in recommender systems. InProc. the 10th International Conference on Intelligent UserInterfaces, Jan. 2005, pp.167-174.

[12] Marsh S P. Formalising trust as a computational concept

[Ph.D. Thesis]. University of Stirling, Apr. 1994.

[13] Mobasher B, Burke R, Bhaumik R, Williams C. Effective at-tack models for shilling item-based collaborative filtering sys-tems. In Proc. WebKDD Workshop, Aug. 2005.

[14] Mobasher B, Burke R, Bhaumik R, Williams C. Toward trust-worthy recommender systems: An analysis of attack modelsand algorithm robustness. ACM Trans. Internet Technol.,Oct. 2007, 7(4): Article No.23.

[15] Cheng Z P, Hurley N. Effective diverse and obfuscated attackson model-based recommender systems. In Proc. the 3rd ACMConf. Recommender Systems, Oct. 2009, pp.141-148.

[16] Cheng Z P, Hurley N. Robustness analysis of model-basedcollaborative filtering systems. In Proc. the 20th Irish Conf.Artificial Intelligence and Cognitive Science, Aug. 2009, pp.3-15.

[17] Ziegler C, Lausen G. Analyzing correlation between trust anduser similarity in online communities. In Proc. the 2nd Int.Conf. Trust Management, May 2004, pp.251-265.

[18] Jamali M, Ester M. TrustWalker: A random walk model forcombining trust-based and item-based recommendation. InProc. the 15th ACM SIGKDD Int. Conf. Knowledge Dis-covery and Data Mining, Jun. 2009, pp.397-406.

[19] Massa P, Avesani P. Trust-aware recommender systems. InProc. the 1st ACM Conference on Recommender Systems,Oct. 2007, pp.17-24.

[20] Ma H, King I, Lyu M R. Learning to recommend with so-cial trust ensemble. In Proc. the 32nd Int. ACM SIGIRConf. Research and Develop. in Inform. Retrieval, Jul.2009, pp.203-210.

[21] Ma H, Lyu M R, King I. Learning to recommend with trustand distrust relationships. In Proc. the 3rd ACM Conferenceon Recommender Systems, Oct. 2009, pp.189-196.

[22] Arthur D, Vassilvitskii S. k-means++: The advantages ofcareful seeding. In Proc. the 18th Annual ACM-SIAM Sym-posium on Discrete Algorithms, Jan. 2007, pp.1027-1035.

[23] Rodgers J, Nicewander A. Thirteen ways to look at the corre-lation coefficient. The American Statistician, 1988, 42: 59-66.
No related articles found!
Full text



No Suggested Reading articles found!

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: jcst@ict.ac.cn
  Copyright ©2015 JCST, All Rights Reserved