Journal of Intelligent Information Systems

, Volume 46, Issue 2, pp 349–367 | Cite as

Robust recommendation method based on suspicious users measurement and multidimensional trust

  • Huawei Yi
  • Fuzhi ZhangEmail author


The existing collaborative recommendation algorithms have poor robustness against shilling attacks. To address this problem, in this paper we propose a robust recommendation method based on suspicious users measurement and multidimensional trust. Firstly, we establish the relevance vector machine classifier according to the user profile features to identify and measure the suspicious users in the user rating database. Secondly, we mine the implicit trust relation among users based on the user-item rating data, and construct a reliable multidimensional trust model by integrating the user suspicion information. Finally, we combine the reliable multidimensional trust model, the neighbor model and matrix factorization model to devise a robust recommendation algorithm. The experimental results on the MovieLens dataset show that the proposed method outperforms the existing methods in terms of both recommendation accuracy and robustness.


Shilling attacks Robust recommendation Multidimensional trust Matrix factorization Relevance vector machine Analytic hierarchy process 


  1. Aghili, G., Shajari, M., Khadivi, S., & Morid, M.A. (2011). Using genre interest of users to detect profile injection attacks in movie recommender systems. In Proceedings of the 10th international conference on Machine Learning and Applications and Workshops (pp. 49–52).Google Scholar
  2. Burke, R., Mobasher, B., Williams, C., & Bhaumik, R. (2006). Classification features for attack detection in collaborative recommender systems. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 542–547).Google Scholar
  3. Cheng, Z., & Hurley, N. (2010). Robust collaborative recommendation by least trimmed squares matrix factorization. In Proceedings of the 22nd IEEE international conference on tools with artificial intelligence (pp. 105–112).Google Scholar
  4. Dyer, R.F. (1992). Group decision support with the analytic hierarchy process. Decision Support Systems, 8(2), 99–124.CrossRefGoogle Scholar
  5. Gunes, I., Kaleli, C., Bilge, A., & Polat, H. (2014). Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 42(4), 767–799.CrossRefGoogle Scholar
  6. Hurley, N.J., O’Mahony, M.P., & Silvestre, G.C. (2007). Attacking recommender systems: A cost-benefit analysis. IEEE Intelligent Systems, 22(3), 64–68.CrossRefGoogle Scholar
  7. Kantor, P.B., Rokach, L., Ricci, F., & Shapira, B. (2011). Recommender systems handbook: Springer.Google Scholar
  8. Kwon, K., Cho, J., & Park, Y (2009). Multidimensional credibility model for neighbor selection in collaborative recommendation. Expert Systems with Applications, 36(3), 7114–7122.CrossRefGoogle Scholar
  9. Mackay, D. (1992). The evidence framework applied to classification networks. Neural computation, 4(5), 720–736.CrossRefGoogle Scholar
  10. Maida, M., Maier, K., Obwegeser, N., & Stix, V. (2012). A multidimensional model of trust in recommender systems. In Proceedings of 13th International Conference on Electronic Commerce and Web Technologies (pp. 212–219).Google Scholar
  11. Mehta, B., Hofmann, T., & Nejdl, W. (2007). Robust collaborative filtering. In Proceedings of the 2007 ACM conference on Recommender systems (pp. 49–56).Google Scholar
  12. Miller, B.N., Albert, I., Lam, S. K., Konstan, J. A., & Riedl, J. (2003). MovieLens unplugged: experiences with an occasionally connected recommender system. In Proceedings of the 8th international conference on Intelligent user interfaces (pp. 263–266).Google Scholar
  13. Mobasher, B., Burke, R., & Sandvig, J.J. (2006). Model-based collaborative filtering as a defense against profile injection attacks. In Proceedings of the 21st national conference on artificial intelligence (pp. 1388–1393).Google Scholar
  14. O’Donovan, J., & Smyth, B. (2005). Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces (pp. 167–174).Google Scholar
  15. O’Mahony, M., Hurley, N., Kushmerick, N., & Silvestre, G. (2004a). Collaborative recommendation: A robustness analysis. ACM Transactions on Internet Technology (TOIT), 4(4), 344–377.CrossRefGoogle Scholar
  16. O’Mahony, M.P., Hurley, N.J., & Silvestre, G.C. (2004b). Efficient and secure collaborative filtering through intelligent neighbour selection. In Proceedings of the 16th European conference on artificial intelligence (pp. 383–387).Google Scholar
  17. O’Mahony, M.P. (2004). Towards robust and efficient automated collaborative filtering. PhD dissertation: University College Dublin.Google Scholar
  18. Pitsilis, G., & Marshall, L.F. (2004). A model of trust derivation from evidence for use in recommendation systems. Computing Science: University of Newcastle upon Tyne.Google Scholar
  19. Sandvig, J.J., Mobasher, B., & Burke, R. (2007). Robustness of collaborative recommendation based on association rule mining. In Proceedings of the 2007 ACM conference on Recommender systems (pp. 105–112).Google Scholar
  20. Tipping, M. (2001a). The relevance vector machine. Advances in Neural Information Processing Systems, 12, 652–658.zbMATHGoogle Scholar
  21. Tipping, M.E. (2001b). Sparse Bayesian learning and the relevance vector machine. The journal of machine learning research, 1, 211–244.MathSciNetzbMATHGoogle Scholar
  22. Weng, J., Miao, C., & Goh, A. (2006). Improving collaborative filtering with trust-based metrics. In Proceedings of the 2006 ACM symposium on Applied computing (pp. 1860–1864).Google Scholar
  23. Williams, C.A., Mobasher, B., & Burke, R. (2007a). Defending recommender systems: detection of profile injection attacks. Service Oriented Computing and Applications, 1(3), 157–170.CrossRefGoogle Scholar
  24. Williams, C.A., Mobasher, B., Burke, R., & Bhaumik, R. (2007b). Detecting profile injection attacks in collaborative filtering: a classification-based approach. In Proceedings of the 8th Knowledge Discovery on the Web International Conference on Advances in Web Mining and Web Usage Analysis (pp. 167–186).Google Scholar
  25. Xu, H., Wu, X., & Li, X. (2009). Comparision study of Internet recommendation system. Journal of Software, 20(2), 350–362.CrossRefGoogle Scholar
  26. Zhang, S., Ouyang, Y., Ford, J., & Makedon, F. (2006). Analysis of a low-dimensional linear model under recommendation attacks. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 517–524).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.The Key Laboratory for Computer Virtual Technology and System Integration of Hebei ProvinceQinhuangdaoChina

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