Finding Reliable Recommendations for Trust Model

  • Weiwei Yuan
  • Donghai Guan
  • Sungyoung Lee
  • Youngkoo Lee
  • Andrey Gavrilov
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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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

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