Preventing Recommendation Attack in Trust-Based Recommender Systems

  • Fu-Guo ZhangEmail author
Short Paper


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.


data lineage “victim” node attack trust propagation 

Supplementary material

11390_2011_181_MOESM1_ESM.pdf (93 kb)
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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

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