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A Social Trust Model Considering Trustees’ Influence

  • Jian-Ping Mei
  • Han Yu
  • Yong Liu
  • Zhiqi Shen
  • Chunyan Miao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8861)

Abstract

Online social networks can be viewed as multi-agent systems (MASs) built on top of social relationships. In these environments, relationships among agents are often formed through trust. Accurately estimating the degree of trust between agents is important in this case as social relationships are frequently leveraged to recommend products or services. Existing social trust models often utilize rating similarity between different agents to calculate how much they should trust each other. However, when a new truster enters the MAS and has not provided sufficient number of ratings, existing approaches cannot effectively advise the truster on which other agents can be trusted. To address this problem, we propose a social trust model that considers a trustee agent’s influence in a social network. Evaluation based on the Epinions dataset shows that the proposed model significantly outperforms a state-of-the-art approach in social recommendation.

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References

  1. 1.
    Au Yeung, C.M., Iwata, T.: Strength of social influence in trust networks in product review sites. In: WSDM 2011, pp. 495–504 (2011)Google Scholar
  2. 2.
    Borzymek, P., Sydow, M., Wierzbicki, A.: Enriching Trust Prediction Model in Social Network with User Rating Similarity. In: CASoN, pp. 40–47 (2009)Google Scholar
  3. 3.
    Hang, C.-W., Wang, Y., Singh, M.P.: Operators for propagating trust and their evaluation in social networks. In: AAMAS, pp. 1025–1032 (2009)Google Scholar
  4. 4.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: KDD, pp. 397–406 (2009)Google Scholar
  5. 5.
    Jamali, M., Ester, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp. 135–142 (2010)Google Scholar
  6. 6.
    Kim, Y.A., Phalak, R.: A trust prediction framework in rating-based experience sharing social networks without a web of trust. Inf. Sci. 191, 128–145 (2012)CrossRefzbMATHGoogle Scholar
  7. 7.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD, pp. 426–434 (2008)Google Scholar
  8. 8.
    Ma, H., King, I., Lyu, M.R., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2009)Google Scholar
  9. 9.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296 (2011)Google Scholar
  10. 10.
    Matsuo, Y., Yamamoto, H.: Community gravity: measuring bidirectional effects by trust and rating on online social networks. In: WWW, pp. 751–760 (2009)Google Scholar
  11. 11.
    Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model of trust and reputation, pp. 188–196 (2002)Google Scholar
  12. 12.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2008)Google Scholar
  13. 13.
    Tang, J., Gao, H., Hu, X., Liu, H.: Exploiting homophily effect for trust prediction. In: WSDM (2013)Google Scholar
  14. 14.
    Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world. In: WSDM, pp. 93–102 (2012)Google Scholar
  15. 15.
    Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: KDD, pp. 1267–1275 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jian-Ping Mei
    • 1
  • Han Yu
    • 2
  • Yong Liu
    • 2
  • Zhiqi Shen
    • 2
  • Chunyan Miao
    • 2
  1. 1.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

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