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A Generative Bayesian Model for Item and User Recommendation in Social Rating Networks with Trust Relationships

  • Gianni Costa
  • Giuseppe Manco
  • Riccardo Ortale
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8724)

Abstract

A Bayesian generative model is presented for recommending interesting items and trustworthy users to the targeted users in social rating networks with asymmetric and directed trust relationships. The proposed model is the first unified approach to the combination of the two recommendation tasks. Within the devised model, each user is associated with two latent-factor vectors, i.e., her susceptibility and expertise. Items are also associated with corresponding latent-factor vector representations. The probabilistic factorization of the rating data and trust relationships is exploited to infer user susceptibility and expertise. Statistical social-network modeling is instead used to constrain the trust relationships from a user to another to be governed by their respective susceptibility and expertise. The inherently ambiguous meaning of unobserved trust relationships between users is suitably disambiguated. An intensive comparative experimentation on real-world social rating networks with trust relationships demonstrates the superior predictive performance of the presented model in terms of RMSE and AUC.

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References

  1. 1.
    Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. The Journal of Machine Learning Research 9, 1981–2014 (2008)zbMATHGoogle Scholar
  2. 2.
    Backstrom, L., Leskovec, J.: Supervised random walks: Predicting and recommending links in social networks. In: Proc. ACM WSDM Conf., pp. 635–644 (2011)Google Scholar
  3. 3.
    Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: Proc. of ACM WSDM Conf., pp. 33–42 (2013)Google Scholar
  4. 4.
    Barbieri, N., Manco, G.: An analysis of probabilistic methods for top-n recommendation in collaborative filtering. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part I. LNCS (LNAI), vol. 6911, pp. 172–187. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Barbieri, N., Manco, G., Ortale, R., Ritacco, E.: Balancing prediction and recommendation accuracy: Hierarchical latent factors for preference data. In: Proc. of SIAM Int. Conf. on Data Mining, pp. 1035–1046 (2012)Google Scholar
  6. 6.
    Costa, G., Ortale, R.: A bayesian hierarchical approach for exploratory analysis of communities and roles in social networks. In: Proc. of the IEEE/ACM ASONAM Conf., pp. 194–201 (2012)Google Scholar
  7. 7.
    Costa, G., Ortale, R.: Probabilistic analysis of communities and inner roles in networks: Bayesian generative models and approximate inference. Social Network Analysis and Mining 3(4), 1015–1038 (2013)CrossRefGoogle Scholar
  8. 8.
    Costa, G., Ortale, R.: A Unified Generative Bayesian Model for Community Discovery and Role Assignment based upon Latent Interaction Factors. In: Proc. of the IEEE/ACM ASONAM Conf. (2014)Google Scholar
  9. 9.
    DeGroot, M.: Optimal Statistical Decisions. McGraw-Hill (1970)Google Scholar
  10. 10.
    Delporte, J., Karatzoglou, A., Matuszczyk, T., Canu, S.: Socially enabled preference learning from implicit feedback data. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part II. LNCS (LNAI), vol. 8189, pp. 145–160. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Gong, N.Z., et al.: Joint link prediction and attribute inference using a social-attribute network. ACM TIST 5(2) (2014)Google Scholar
  12. 12.
    Griffiths, T.L., Ghahramani, Z.: The indian buffet process: An introduction and review. The Journal of Machine Learning Research 12, 1185–1224 (2011)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Hofman, T., Puzicha, J., Jordan, M.I.: Learning from dyadic data. In: Proc. NIPS Conf., pp. 466–472 (1999)Google Scholar
  14. 14.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proc. of ACM RECSYS Conf., pp. 135–142 (2010)Google Scholar
  15. 15.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  16. 16.
    Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer (2001)Google Scholar
  17. 17.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proc. of Int. ACM SIGIR Conf., pp. 203–210 (2009)Google Scholar
  18. 18.
    Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: Social recommendation using probabilistic matrix factorization. In: Proc. of ACM CIKM Conf., pp. 931–940 (2008)Google Scholar
  19. 19.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proc. of ACM WSDM Conf., pp. 287–296 (2011)Google Scholar
  20. 20.
    Menon, A.K., Elkan, C.: Link prediction via matrix factorization. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS (LNAI), vol. 6912, pp. 437–452. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Miller, K.T., Griffiths, T.L., Jordan, M.I.: Nonparametric latent feature models for link prediction. In: Proc. NIPS Conf., pp. 1276–1284 (2009)Google Scholar
  22. 22.
    Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R.M., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Proc. IEEE ICDM Conf., pp. 502–511 (2008)Google Scholar
  23. 23.
    Purushotham, S., Liu, Y., Kuo, C.C.J.: Collaborative topic regression with social matrix factorization for recommendation systems. In: Proc. ICML Conf., pp. 759–766 (2012)Google Scholar
  24. 24.
    Rendle, S., Christoph, F., Zeno, G., Lars, S.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proc. UAI Conf. (2009)Google Scholar
  25. 25.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proc. ICML Conf., pp. 880–887 (2008)Google Scholar
  26. 26.
    Badrul, M.: Sarwar et al. Application of dimensionality reduction in recommender system – a case study. In: ACM WEBKDD Workshop (2000)Google Scholar
  27. 27.
    Shen, Y., Jin, R.: Learning personal+social latent factor model for social recommendation. In: Proc. of ACM SIGKDD Conf., pp. 1303–1311 (2012)Google Scholar
  28. 28.
    Sindhwani, V., Bucak, S.S., Hu, J., Mojsilovic, A.: One-class matrix completion with low-density factorizations. In: Proc. of IEEE ICDM Conf., pp. 1055–1060 (2010)Google Scholar
  29. 29.
    Tang, J., Gao, H., Liu, H.: mtrust: Discerning multi-faceted trust in a connected world. In: Proc. ACM WSDM Conf., pp. 93–102 (2012)Google Scholar
  30. 30.
    Yang, S.-H., et al.: Like like alike: Joint friendship and interest propagation in social networks. In: Proc. WWW Conf., pp. 537–546 (2011)Google Scholar
  31. 31.
    Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: Proc. ACM SIGKDD Conf., pp. 1267–1275 (2012)Google Scholar
  32. 32.
    Zhu, J.: Max-margin nonparametric latent feature models for link prediction. In: Proc. NIPS Conf., pp. 719–726 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gianni Costa
    • 1
  • Giuseppe Manco
    • 1
  • Riccardo Ortale
    • 1
  1. 1.ICAR-CNRRende (CS)Italy

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