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Research Articles

Robust analysis of trust-based recommendation algorithms

  • CHEN Su ,
  • LUO Tie-Jian ,
  • XU Yan-Xiang
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  • School of Information Science and Engineering, Graduate University, Chinese Academy of Sciences, Beijing 100049, China

Received date: 2010-03-17

  Revised date: 2010-05-21

  Online published: 2011-03-15

Supported by

Supported by the e-Education Project (0826011ED2) granted by the Chinese Academy of Sciences

Abstract

Trust-based recommendation is an emerging technique,in which the trust web of users serves as an overlay to locate reliable advisers.Although this technique is claimed to be more robust than collaborative filtering in previous researches,its real strength to resist attacks has not been quantifiably studied.We propose a formal evaluation framework for this topic and compare two representative algorithms in the literature on the data set from Epinions. com.Experiments indicate the key factors for their robustness. Furthermore,several countermeasures are suggested based on these findings.

Cite this article

CHEN Su , LUO Tie-Jian , XU Yan-Xiang . Robust analysis of trust-based recommendation algorithms[J]. Journal of University of Chinese Academy of Sciences, 2011 , 28(2) : 253 -261 . DOI: 10.7523/j.issn.2095-6134.2011.2.018

References


[1] Breese J,Heckerman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering //Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI).San Francisco,1998:43-52.

[2] Das A, Datar M,Garg A.Google news personalization: Scalable online collaborative filtering //Proceedings of the Sixth International World Wide Web Conference.Banff,Alberta,Canada,2007:272-280.

[3] Linden G,Smith B,York J.Amazon.com recommendation: Item-to-item collaborative filtering
[J].IEEE Internet Computing, 2003:76-80.

[4] Huang Z,Chen H,Zeng D.Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
[J].ACM Trans Inf Syst,2004,22(1):116-142.

[5] O’Mahony M, Hurley N, Kushmerick N,et al.Collaborative recommendation: A robustness analysis
[J]. ACM Transaction on Internet Technology, 2004,4(4):344-377.

[6] Massa P,Avesani P.Trust-aware recommender systems //Proceedings of RecSys’07.Minneapolis,Minnesota,USA,2007.

[7] Ziegler C.Towards decentralized recommender system .Freibug:Albert-Ludwigs-University,2005.

[8] Chen S,Luo T J,Liu W,et al.Incorporating similarity and trust for collaborative filtering //Proceedings of IEEE FSKD’09.Tianjin,China,2009.

[9] Golbeck J.Computing and applying trust in web-based social networks .Adephi:University of Maryland,2005.

[10] Guha R.Open rating systems .Stanford,CA,USA:Stanford Knowledge Systems Laboratory,2003.

[11] Josang A,Ismail R,Boyd C.Survey of trust and reputation systems for Online service provision
[J].Decision Support Systems,2007,43(2):618-644.

[12] Yu H F,Kaminsky M,Gibbons P,et al.SybilGuard:Defending against sybil attacks via social Network //Proceedings of the ACM SIGCOMM 2006.Pisa,Italy,2006.

[13] Cheng A,Friedman E.Sybilproof reputation mechanisms //Proceedings of ACM SIGCOMM Workshop on Economics of Peer-to-Peer Systems.2005.

[14] Newsome J,Shi E,Song D,et al.The Sybil attack in sensor networks:Analysis & defenses //ACM/IEEE IPSN.2004.

[15] Leskovec J.Dynamics of large networks .Pittsburgh:Carnegie Mellon University,2008.

[16] Chen S,Luo T J,Zhu T S.A second-order Markov random walk approach for collaborative filtering //Proceedings of IEEE SocialCom’09.Vancouver,Canada,2009.

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