메뉴 건너뛰기




Volumn 22, Issue 4, 2012, Pages 945-957

A hierarchical model for ordinal matrix factorization

Author keywords

Bayesian inference; Collaborative filtering; Gibbs sampling; Hierarchial modelling; Large scale machine learning; Low rank matrix decomposition; Ordinal regression; Variational Bayes

Indexed keywords


EID: 84859766908     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-011-9264-x     Document Type: Article
Times cited : (24)

References (35)
  • 1
    • 84916537550 scopus 로고
    • Bayesian analysis of binary and polychotomous response data
    • Albert, J. H., Chib, S.: Bayesian analysis of binary and polychotomous response data. J. Am. Stat. Assoc. 88(422), 669-679 (1993).
    • (1993) J. Am. Stat. Assoc. , vol.88 , Issue.422 , pp. 669-679
    • Albert, J.H.1    Chib, S.2
  • 6
    • 21844453228 scopus 로고    scopus 로고
    • Gaussian processes for ordinal regression
    • Chu, W., Ghahramani, Z.: Gaussian processes for ordinal regression. J. Mach. Learn. Res. 6, 1019-1041 (2005).
    • (2005) J. Mach. Learn. Res. , vol.6 , pp. 1019-1041
    • Chu, W.1    Ghahramani, Z.2
  • 7
    • 0021518209 scopus 로고
    • Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
    • Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721-741 (1984).
    • (1984) IEEE Trans. Pattern Anal. Mach. Intell. , vol.6 , pp. 721-741
    • Geman, S.1    Geman, D.2
  • 9
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., Saul, L. K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183-233 (1999).
    • (1999) Mach. Learn. , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 11
    • 71149119166 scopus 로고    scopus 로고
    • Non-linear matrix factorization with Gaussian processes
    • L. Bottou and M. Littman (Eds.), San Francisco: Morgan Kauffman
    • Lawrence, N. D., Urtasun, R.: Non-linear matrix factorization with Gaussian processes. In: Bottou, L., Littman, M. (eds.) Proceedings of the International Conference in Machine Learning. Morgan Kauffman, San Francisco (2009).
    • (2009) Proceedings of the International Conference in Machine Learning
    • Lawrence, N.D.1    Urtasun, R.2
  • 13
    • 0037252945 scopus 로고    scopus 로고
    • Amazon.com recommendations: Item-to-item collaborative filtering
    • Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76-80 (2003).
    • (2003) IEEE Internet Comput. , vol.7 , Issue.1 , pp. 76-80
    • Linden, G.1    Smith, B.2    York, J.3
  • 15
    • 84898970398 scopus 로고    scopus 로고
    • Modeling user rating profiles for collaborative filtering
    • S. Thrun, L. Saul, B. Schölkopf (Eds.), Cambridge: MIT Press
    • Marlin, B.: Modeling user rating profiles for collaborative filtering. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge (2004).
    • (2004) Advances in Neural Information Processing Systems , pp. 16
    • Marlin, B.1
  • 24
    • 85161989354 scopus 로고    scopus 로고
    • Probabilistic matrix factorization
    • J. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Cambridge: MIT Press
    • Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems, vol. 20, pp. 1257-1264. MIT Press, Cambridge (2008b).
    • (2008) Advances in Neural Information Processing Systems , vol.20 , pp. 1257-1264
    • Salakhutdinov, R.1    Mnih, A.2
  • 29
    • 33750516992 scopus 로고
    • On the theory of scales of measurement
    • Stevens, S. S.: On the theory of scales of measurement. Science 103(2684), 677-680 (1946).
    • (1946) Science , vol.103 , Issue.2684 , pp. 677-680
    • Stevens, S.S.1
  • 30
    • 64149121935 scopus 로고    scopus 로고
    • Scalable collaborative filtering approaches for large recommender systems
    • Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623-656 (2009).
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 623-656
    • Takács, G.1    Pilászy, I.2    Németh, B.3    Tikk, D.4
  • 32
    • 71149088913 scopus 로고    scopus 로고
    • Large-scale collaborative prediction using a nonparametric random effects model
    • L. Bottou and M. Littman (Eds.), San Francisco: Morgan Kauffman
    • Yu, K., Lafferty, J., Zhu, S., Gong, Y.: Large-scale collaborative prediction using a nonparametric random effects model. In: Bottou, L., Littman, M. (eds.) Proceedings of the International Conference in Machine Learning. Morgan Kauffman, San Francisco (2009a).
    • (2009) Proceedings of the International Conference in Machine Learning
    • Yu, K.1    Lafferty, J.2    Zhu, S.3    Gong, Y.4
  • 35
    • 83855162681 scopus 로고    scopus 로고
    • Stochastic relational models for large-scale dyadic data using MCMC
    • D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou (Eds.)
    • Zhu, S., Yu, K., Gong, Y.: Stochastic relational models for large-scale dyadic data using MCMC. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 21, pp. 1993-2000 (2009).
    • (2009) Advances in Neural Information Processing Systems , vol.21 , pp. 1993-2000
    • Zhu, S.1    Yu, K.2    Gong, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.