How Far Are We in Trust-Aware Recommendation?

  • Yue Shi
  • Martha Larson
  • Alan Hanjalic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)


Social trust holds great potential for improving recommendation and much recent work focuses on the use of social trust for rating prediction, in particular, in the context of the Epinions dataset. An experimental comparison with trust-free, naïve approaches suggests that state-of-the-art social-trust-aware recommendation approaches, in particular Social Trust Ensemble (STE), can fail to isolate the true added value of trust. We demonstrate experimentally that not only trust-set users, but also random users can be exploited to yield recommendation improvement via STE. Specific users, however, do benefit from use of social trust, and we conclude with an investigation of their characteristics.


Recommender systems social trust trust-aware recommendation 


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  1. 1.
    Liu, N.N., Cao, B., Zhao, M., Yang, Q.: Adapting neighborhood and matrix factorization models for context aware recommendation. In: CAMRa 2010, pp. 7–13 (2010)Google Scholar
  2. 2.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR 2009, pp. 203–210 (2009)Google Scholar
  3. 3.
    Ma, H., Lyu, M.R., King, I.: Learning to recommend with trust and distrust relationships. In: RecSys 2009, pp. 189–196 (2009)Google Scholar
  4. 4.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: Social recommendation using probabilistic matrix factorization. In: CIKM 2008, pp. 931–940 (2008)Google Scholar
  5. 5.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: RecSys 2007, pp. 17–24 (2007)Google Scholar
  6. 6.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: IUI 2005, pp. 167–174 (2005)Google Scholar
  7. 7.
    Shi, Y., Larson, M., Hanjalic, A.: Towards understanding the challenges facing effective trust-aware recommendation. In: RSWeb 2010, pp. 40–43 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yue Shi
    • 1
  • Martha Larson
    • 1
  • Alan Hanjalic
    • 1
  1. 1.Multimedia Information Retrieval LabDelft University of TechnologyDelftNetherlands

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