SLGP Header


IJCSEC Front Page

Trust is an important factor that is used as a criterion for service selection. It creates service as well as building block for many real applications. Existing trust model either use properties like transitivity, multi-aspect, bias to do trust evaluation or user preferences for different quality of service is consider for trust evaluation these leads to security bleach in real application. In proposed system a trust model is created to combine both user preferences QOS and important properties of trust evaluation are considered to do trust evaluation this model is applicable to both binary and continuous scenario and works well on trust evaluation, and can reduce the malicious ratings. This method evaluates on data sets show to achieves improvement over several existing benchmark, for both numerical trustworthiness scores and predicting binary and continuous trust/distrust signs.
Index Terms::Trust inference, trust prediction, transitivity property, multi-aspect property, latent factors, trust bias.
Trust is essential to reduce uncertainty and boost many real-world existing applications such as social networks, peer-to-peer networks, e-commerce and semantics web, etc. Trust inference can be calculated by using three properties such as transitivity, multi-aspect, trust bias. The basic assumption behind most of the existing trust inference methods is the transitivity property of trust, which is rooted in the social structural balance theory. For example, Alice trust Bob and Bob trust carol, Alice might also trust carol to some extent. This model as a whole, have been widely studied and successfully applied in many real-world settings. In additional transitivity property explores another equal important property, that is the multi aspect of trust, composes of multiple factors, and different users may have different preference factors. For example in ecommerce some users might care about delivery time, some of them might care about product quality; some others give a higher ratings to the factors of product quality. Finally, this model to enhance trust model to improves inference models.Main contribution of this paper are summarised as follows: 1) Trust Models: We proposed a trust model to integrates transitivity, multi-aspect and trust bias into one single trust inference model. 2) Inference Algorithms: We proposed a family of algorithms. It finds a local optimal solution with linear complexity. It applies to both binary and continuous trust inference scenarios. 3) Performance Improvements: we conducted extensive experimental evaluations on data sets, it improves significant performance improvements in both continuous and binary inference models. In continuous, for example, our MATRI outperforms the best known existing methods by 26.7%-40.7% in terms of prediction accuracy; and by computation, our MATRI is much faster in terms of on-line response, achieving upto 7 orders of magnitude speedup. Accordingly, trust is introduced to resolve the web service problem [5,6]. There are two lines of research [7,8]; The first line is based on the “credential-based” approach for traditional identity authentication mechanism. This approach ensured such as PolicyMaker[7], KeyNote[8] and their derived methods. In this case, the “Credential-Based” approach is not capable to provide a rational trust evaluation result. This focus on “experience-Based” approach. The second line is based on the “experience based” approach. Some researchers point out that trust model should be irrational and should take subjective factors into consideration.


  1. [1] C. Ziegler and G. Lausen, “Propagation models for trust and distrust in social networks,” Inform. Syst. Front., vol. 7, no. 4, pp.337–358, 2005.
  2. A. Jøsang and R. Ismail, “The Beta reputation system,” in Proc. 15th Bled Electron. Comm. Conf., vol. 160. Bled, Slovenia, Jun. 2002.
  3. S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The Eigentrust algorithm for reputation management in P2P net-works,” in Proc. 12th Int. Conf. WWW, Budapest, Hungary, 2003, pp.640–651.
  4. M. Richardson, R. Agrawal, and P. Domingos, “Trust management for the semantic web,” in Proc. 2nd ISWC, Sanibel Island, FL, USA, 2003, pp. 351–368.
  5. D. Cartwright and F. Harary, “Structural balance: A generalization of Heider’s theory,” Psychol. Rev., vol. 63, no. 5, pp. 277–293, 1956.
  6. G. Liu, Y. Wang, and M. Orgun, “Trust transitivity in complex social networks,” in Proc. AAAI, 2011, pp. 1222–1229.
  7. D. Gefen, “Reflections on the dimensions of trust and trustwor-thiness among online consumers,” ACM SIGMIS Database, vol. 33, no. 3, pp. 38–53, 2002.
  8. D. Sirdeshmukh, J. Singh, and B. Sabol, “Consumer trust, value, and loyalty in relational exchanges,” J. Marketing, vol. 66, no. 1, pp.15–37, 2002.
  9. A. Tversky and D. Kahneman, “Judgment under uncertainty: Heuristics and biases,” Sci., vol. 185, no. 4157, pp. 1124–1131, 1974.
  10. Y. Yao, H. Tong, F. Xu, and J. Lu, “Subgraph extraction for trust inference in social networks,” in Proc. IEEE/ACM Int. Conf. ASONAM, Istanbul, Turkey, 2012, pp. 163–170.
  11. L. Xiong and L. Liu, “Peertrust: Supporting reputation-based trust for peer-to-peer electronic communities,” IEEE Trans. Knowl. Data Eng., vol. 16, no. 7, pp. 843–857, Jul. 2004.
  12. J. Tang, H. Gao, and H. Liu, “mTrust: Discerning multi-faceted trust in a connected world,” in Proc. 5th ACM Int. Conf. WSDM, Washingtion, DC, USA, 2012, pp. 93–102.
  13. V. Nguyen, E. Lim, J. Jiang, and A. Sun, “To trust or not to trust? Predicting online trusts using trust antecedent framework,” in Proc. 9th IEEE ICDM, Miami, FL, USA, 2009, pp. 896–901.
  14. Y. Koren, “Factorization meets the neighborhood: A multifaceted collaborative filtering model,” in Proc. 14th ACM SIGKDD Int. Conf. KDD, New York, NY, USA, 2008, pp. 426–434.
  15. R. Guha, R. Kumar, P. Raghavan, and A. Tomkins, “Propagation of trust and distrust,” in Proc. 13th Int. Conf. WWW, New York, NY, USA, 2004, pp. 403–412.
  16. Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Comput., vol. 42, no. 8, pp. 30–37, 2009.
  17. P. Massa and P. Avesani, “Controversial users demand local trust metrics: An experimental study on epinions. com community,” in Proc. 20th Nat. Conf. AAAI, 2005, pp. 121–126.
  18. B. Lang, “A computational trust model for access control in P2P,” Sci. China Inform. Sci., vol. 53, no. 5, pp. 896–910, 2010.
  19. R. Bell, Y. Koren, and C. Volinsky, “Modeling relationships at mul-tiple scales to improve accuracy of large recommender systems,” in Proc. 13th ACM SIGKDD Int. Conf. KDD, New York, NY, USA, 2007, pp. 95–104.
  20. H. Ma, M. Lyu, and I. King, “Learning to recommend with trust and distrust relationships,” in Proc. 3rd ACM Conf. RecSys, New York, NY, USA, 2009, pp. 189–196.
  21. A. Buchanan and A. Fitzgibbon, “Damped Newton algorithms for matrix factorization with missing data,” in Proc. IEEE CVPR, vol. 2. Washington, DC, USA, 2005, pp. 316–322.
  22. X. Liu, A. Datta, K. Rzadca, and E. Lim, “Stereotrust: A group based personalized trust model,” in Proc. 18th ACM CIKM, Hong Kong, China, 2009, pp. 7–16.
  23. D. Watts and S. Strogatz, “Collective dynamics of ’small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998.
  24. J. Leskovec, J. Kleinberg, and C. Faloutsos, “Graphs over time: Densification laws, shrinking diameters and possible explana-tions,” in Proc. 11th ACM SIGKDD Int. Conf. KDD, Chicago, IL, USA, 2005, pp. 177–187.
  25. C.-W. Hang, Y. Wang, and M. P. Singh, “Operators for propagating trust and their evaluation in social networks,” in Proc. 8th Int. Conf. AAMAS, Budapest, Hungary, 2009, pp. 1025–1032.
  26. J. Leskovec, D. Huttenlocher, and J. Kleinberg, “Predicting posi-tive and negative links in online social networks,” in Proc. 19th Int. Conf. WWW, Raleigh, NC, USA, 2010, pp. 641–650.
  27. Y. Wang and M. P. Singh, “Trust representation and aggregation in a distributed agent system,” in Proc. 21st Nat. Conf. AAAI, 2006, pp.1425–1430.
  28. Y. Wang and M. P. Singh, “Formal trust model for multiagent systems,” in Proc. 20th IJCAI, San Francisco, CA, USA, 2007, pp.1551–1556.
  29. C. Hsieh, K. Chiang, and I. Dhillon, “Low rank modeling of signed networks,” in Proc. 18th ACM SIGKDD Int. Conf. KDD, Beijing, China, 2012, pp. 507–515.
  30. K.-Y. Chiang, N. Natarajan, A. Tewari, and I. S. Dhillon, “Exploiting longer cycles for link prediction in signed net-works,” in Proc. 20th ACM CIKM, Glasgow, Scotland, U.K., 2011, pp.1157–1162.