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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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)
Backstrom, L., Leskovec, J.: Supervised random walks: Predicting and recommending links in social networks. In: Proc. ACM WSDM Conf., pp. 635–644 (2011)
Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: Proc. of ACM WSDM Conf., pp. 33–42 (2013)
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)
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)
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)
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)
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)
DeGroot, M.: Optimal Statistical Decisions. McGraw-Hill (1970)
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)
Gong, N.Z., et al.: Joint link prediction and attribute inference using a social-attribute network. ACM TIST 5(2) (2014)
Griffiths, T.L., Ghahramani, Z.: The indian buffet process: An introduction and review. The Journal of Machine Learning Research 12, 1185–1224 (2011)
Hofman, T., Puzicha, J., Jordan, M.I.: Learning from dyadic data. In: Proc. NIPS Conf., pp. 466–472 (1999)
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)
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)
Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer (2001)
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)
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)
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)
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)
Miller, K.T., Griffiths, T.L., Jordan, M.I.: Nonparametric latent feature models for link prediction. In: Proc. NIPS Conf., pp. 1276–1284 (2009)
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)
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)
Rendle, S., Christoph, F., Zeno, G., Lars, S.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proc. UAI Conf. (2009)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proc. ICML Conf., pp. 880–887 (2008)
Badrul, M.: Sarwar et al. Application of dimensionality reduction in recommender system – a case study. In: ACM WEBKDD Workshop (2000)
Shen, Y., Jin, R.: Learning personal+social latent factor model for social recommendation. In: Proc. of ACM SIGKDD Conf., pp. 1303–1311 (2012)
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)
Tang, J., Gao, H., Liu, H.: mtrust: Discerning multi-faceted trust in a connected world. In: Proc. ACM WSDM Conf., pp. 93–102 (2012)
Yang, S.-H., et al.: Like like alike: Joint friendship and interest propagation in social networks. In: Proc. WWW Conf., pp. 537–546 (2011)
Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: Proc. ACM SIGKDD Conf., pp. 1267–1275 (2012)
Zhu, J.: Max-margin nonparametric latent feature models for link prediction. In: Proc. NIPS Conf., pp. 719–726 (2012)
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Costa, G., Manco, G., Ortale, R. (2014). A Generative Bayesian Model for Item and User Recommendation in Social Rating Networks with Trust Relationships. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44848-9_17
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DOI: https://doi.org/10.1007/978-3-662-44848-9_17
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