Finding Reliable Recommendations for Trust Model

  • Weiwei Yuan
  • Donghai Guan
  • Sungyoung Lee
  • Youngkoo Lee
  • Andrey Gavrilov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4255)


This paper presents a novel context-based approach to find reliable recommendations for trust model in ubiquitous environments. Context is used in our approach to analyze the user’s activity, state and intention. Incremental learning based neural network is used to dispose the context in order to detect doubtful recommendations. This approach has distinct advantages when dealing with randomly given irresponsible recommendations, individual unfair recommendations as well as unfair recommendations flooding regardless of from recommenders who always give malicious recommendations or “inside job” (recommenders who acted honest previous suddenly give unfair recommendations), which is lack  of consideration in the previous works. The incremental learning based neural network used in our approach also enables to filter out the unfair recommendations with limited information about the recommenders. Our simulation results show that our approach can effectively find reliable recommendations in different scenarios and a comparison is also given between previous works and our method.


Trust Model Service Requester Final Recommendation Ubiquitous Environment Rate Reputation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Polikar, R., Udpa, L., Udpa, S.S., Honavar, V.: learn++: An Incremental Learning Algorithm for Supervised Neural Networks. IEEE transactions on systems, man, and cybernetics-Part C: Applications and Reviews 31(4) (November 2001)Google Scholar
  2. 2.
    Fahlman, S.E., Lebiere, C.: The Cascade-Correlation Learning Architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon UniversityGoogle Scholar
  3. 3.
    Ngo, H.Q., Shehzad, A., Kiani, S.L., Riaz, M., Ngoc, K.A., Lee, S.: Developing Context-aware Ubiquitous Computing Systems with a Unified Middleware Frame Work. In: Yang, L.T., Guo, M., Gao, G.R., Jha, N.K. (eds.) EUC 2004. LNCS, vol. 3207, pp. 672–681. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Damiani, Vimercati, Paraboschi, Samarati, Violante: A reputation-based approach for choosing reliable resources in peer-to-peer networks. In: 9th ACM CCS (2002)Google Scholar
  5. 5.
    Yu, B., Singh, M.P., Sycara, K.: Developing trust large-scale peer-to-peer systems. In: First IEEE Symposium on Multiagent Security and Survivability (2004)Google Scholar
  6. 6.
    Xu, P., Gao, J., Guo, H.: Rating Reputation: a necessary consideration in reputation mechanism. In: Yeung, D.S., Liu, Z.-Q., Wang, X.-Z., Yan, H. (eds.) ICMLC 2005. LNCS, vol. 3930. Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Whitby, A., Josang, A., Indulska, J.: Filtering out unfair ratings in Bayesian reputation systems. In: AAMAS 2004, New York, USA (2004)Google Scholar
  8. 8.
    Song, W., Phoha, V.V., Xu, X.: An adaptive recommendation trust model in multiagent system. In: IEEE/WIC/ACM IAT 2004 (2004)Google Scholar
  9. 9.
    Song, W., Phoha, V.V.: Neural network-based reputation model in a distributed system. In: 2004 IEEE International Conference on E-Commerce Technology (CEC 2004), pp. 321–324 (2004)Google Scholar
  10. 10.
    Baohua, H., Heping, H., Zhengding, L.: Identifying local trust value with neural network in p2p environment. In: The First IEEE and IFIP International Conference in Central Asia on Internet (2005)Google Scholar
  11. 11.
    Dellarocas, C.: The design of reliable trust management systems for electronic trading communities. MIT Working PaperGoogle Scholar
  12. 12.
    Dellarocas, C.: Building trust online: the design of robust reputation reporting mechanisms for online trading communities. In: Doukidis, G., Mylonopoulos, N., Pouloudi, N. (eds.) A combined perspective on the digital era. Idea Book Publishing (2004)Google Scholar
  13. 13.
    Dellarocas, C.: Immunizing online Reputation Reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the ACM Conference on Electronic Commerce, Minneapolis, Minnesota, USA, pp. 150–157 (2000)Google Scholar
  14. 14.
    Dellarocas, C.: Mechanisms for coping with unfair ratings and discriminatory behavior in online reputation reporting systems. In: ICIS, pp. 520–525 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weiwei Yuan
    • 1
  • Donghai Guan
    • 1
  • Sungyoung Lee
    • 1
  • Youngkoo Lee
    • 1
  • Andrey Gavrilov
    • 1
  1. 1.Department of Computer EngineeringKyung Hee UniversityKorea

Personalised recommendations