Advertisement

Preventing Recommendation Attack in Trust-Based Recommender Systems

  • Fu-Guo ZhangEmail author
Short Paper

Abstract

Despite its success, similarity-based collaborative filtering suffers from some limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system. To the best of our knowledge, there has not any prior study on recommendation attack in a trust-based recommender system. We analyze the attack problem, and find that “victim” nodes play a significant role in the attack. Furthermore, we propose a data provenance method to trace malicious users and identify the “victim” nodes as distrust users of recommender system. Feasibility study of the defend method is done with the dataset crawled from Epinions website.

Keywords

data lineage “victim” node attack trust propagation 

Supplementary material

11390_2011_181_MOESM1_ESM.pdf (93 kb)
(PDF 93.2 kb)

References

  1. [1]
    Schafer J B, Konstan J, Riedl J. Recommender systems in e-commerce. In Proc. the 1st ACM Conference on Electronic Commerce, New York, USA, Nov. 3–5, 1999, pp.158-166.Google Scholar
  2. [2]
    Sinha R, Swearingen K. Comparing recommendations made by online systems and friends. In Proc. the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries, Dublin, Ireland, Jun. 18–20, 2001.Google Scholar
  3. [3]
    Massa P, Avesani P. Trust-aware collaborative filtering for recommender systems. In Proc. International Conference on Cooperative Information Systems, Agia Napa, Cyprus, Oct. 25–29, 2004, pp.492-508.Google Scholar
  4. [4]
    Massa P, Bhattacharjee B. Using trust in recommender systems: An experimental analysis. In Proc. the 2nd International Conference on Trust Management, Oxford, UK, Mar. 29-Apr. 1, 2004, pp.221-235.Google Scholar
  5. [5]
    Fuguo Z. Research on trust based collaborative filtering algorithm for user's multiple interests. Journal of Chinese Computer System, 2008, 29(8): 1415–1419.Google Scholar
  6. [6]
    Lam S, Reidl J. Shilling recommender systems for fun and profit. In Proc. the 13th International Conference on WWW, New York, USA, May 17–20, 2004, pp.393-402.Google Scholar
  7. [7]
    Milgram S. The small world problem. Psychology Today, 1967, 2: 60–67.Google Scholar
  8. [8]
    Gyöngyi Z, Garcia-Molina H, Pedersen J. Combating Web spam with TrustRank. In Proc. the 30th International Conference on Very Large Data Bases, Toronto, Canada, Aug. 31-Sept. 3, 2004, pp.576-587.Google Scholar
  9. [9]
    Krishnan V, Raj R. Web spam detection with anti-trust rank. In Proc. the Workshop on Adversarial Information Retrieval on the Web, Seattle, USA, Aug. 10, 2006.Google Scholar
  10. [10]
    Wu B, Goel V, Davison B D. Topical TrustRank: Using topicality to combat web spam. In Proc. the 15th Int. Conf. World Wide Web, Edinburgh, UK, May 23–26, 2006, pp.63-72.Google Scholar
  11. [11]
    Srivatsa M, Xiong L, Liu L. Trust guard: Countering vulnerabilities in reputation management for decentralized overlay networks. In Proc. the 14th Conf. World Wide Web (WWW 2005), Chiba, Japan, May 10–14, 2005, pp.422-431.Google Scholar
  12. [12]
    Levien R. Advogato trust metric [Ph.D. Dissertation]. UC Berkeley, USA, 2003.Google Scholar
  13. [13]
  14. [14]
    Ziegler C N, Lausen G. Propagation models for trust and distrust in social networks. Information Systems Frontiers, 2005, 7(4/5): 337–358.CrossRefGoogle Scholar
  15. [15]
    Jøsang A, Gray E, Kinateder M. Analysing topologies of transitive trust. In Proc. the Workshop of Formal Aspects of Security and Trust, Pisa, Italy, Sept. 8–9, 2003.Google Scholar
  16. [16]
    Victor P, Cornelis C, Cock M D et al. Gradual trust and distrust in recommender systems. Fuzzy Sets and Systems, 2009, 160(10): 1367–1382.MathSciNetzbMATHCrossRefGoogle Scholar
  17. [17]
    Abdul-Rahman A, Hailes S. A distributed trust model. In Proc. the 1997 Workshop on New Security Paradigms, Lang-dale, UK, Sept. 23–26, 1997, pp.48-60.Google Scholar
  18. [18]
    Chen R, Yeager W. Poblano: A distributed trust model for peer-to-peer networks. Technical Report, Sun Microsystems, Santa Clara, USA, Feb. 2003.Google Scholar
  19. [19]
    Aberer K, Despotovic Z. Managing trust in a peer-2-peer information system. In Proc. the 10th International Conference on Information and Knowledge Management, Atlanta, USA, Nov. 5–10, 2001, pp.310-317.Google Scholar
  20. [20]
    Golbeck J, Parsia B, Hendler J. Trust networks on the semantic web. In Proc. Cooperative Intelligent Agents, Helsinki, Finland, Aug. 27–29, 2003.Google Scholar
  21. [21]
    Guha R. Open rating systems. Technical Report, Stanford Knowledge Systems Laboratory, Stanford, USA, 2003.Google Scholar
  22. [22]
    Guha R, Kumar R, Raghavan P et al. Propagation of trust and distrust. In Proc. the 13th Annual International World Wide Web Conference, New York, USA, May 17–22, 2004, pp.403-412.Google Scholar
  23. [23]
    Page L, Brin S, Motwani R et al. The pagerank citation ranking: Bringing order to the web. Technical Report, Stanford Digital Library Technologies Project, 1998.Google Scholar
  24. [24]
    Woodrufifi A, Stonebraker M. Supporting fine-grained data lineage in a database visualization environment. In Proc. the 13th Conference on Data Engnieering (IEEE ICDE), Birminghm, UK, Apr. 7–11, 1997, pp.91-102.Google Scholar
  25. [25]
    Herlocker J L, Konstan J A, Riedl J. Explaining collaborative filtering recommendations. In Proc. CSCW 2000, Philadelphia, USA, Dec. 2–6, 2000, pp.241-250.Google Scholar

Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2011

Authors and Affiliations

  1. 1.School of Information and TechnologyJiangxi University of Finance and EconomicsNanchangChina
  2. 2.Institute of Information Resource ManagementJiangxi University of Finance and EconomicsNanchangChina
  3. 3.Jiangxi Key Laboratory of Data and Knowledge EngineeringJiangxi University of Finance and EconomicsNanchangChina

Personalised recommendations