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Volumn 3, Issue , 2008, Pages 1487-1490

Multi-HDP: A non parametric bayesian model for tensor factorization

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN MODELS; BAYESIAN PROBABILISTIC MODELS; GIBBS SAMPLERS; LATENT STRUCTURES; LDA MODELS; LOW DIMENSIONAL; MACHINE LEARNING COMMUNITIES; MATRIX FACTORIZATIONS; MISSING DATUMS; PARAMETRIC FORMS; TENSOR FACTORIZATIONS;

EID: 57749104755     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (58)

References (10)
  • 4
    • 3042742744 scopus 로고    scopus 로고
    • Latent semantic models for collaborative filtering
    • Hofmann, T. 2004. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1):89-115.
    • (2004) ACM Trans. Inf. Syst , vol.22 , Issue.1 , pp. 89-115
    • Hofmann, T.1
  • 8
    • 84864028297 scopus 로고    scopus 로고
    • Modeling dyadic data with binary latent factors
    • Schölkopf, B, Platt, J, and Hoffman, T, eds, Cambridge, MA: MIT Press
    • Meeds, E.; Ghahramani, Z.; Neal, R. M.; and Roweis, S. T. 2007. Modeling dyadic data with binary latent factors. In Schölkopf, B.; Platt, J.; and Hoffman, T., eds., Advances in Neural Information Processing Systems 19. Cambridge, MA: MIT Press. 977-984.
    • (2007) Advances in Neural Information Processing Systems 19 , pp. 977-984
    • Meeds, E.1    Ghahramani, Z.2    Neal, R.M.3    Roweis, S.T.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.