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Volumn AIDSS, Issue , 2007, Pages 145-151

Learning to rank for collaborative filtering

Author keywords

Collaborative Filtering; Machine Learning; Ranking; Recommender Systems

Indexed keywords

COLLABORATIVE FILTERING; DATA SETS; EFFECTIVE ALGORITHMS; ERROR FUNCTION; EVALUATION PROTOCOL; LEARNING TO RANK; MACHINE LEARNING; PREDICTION BASED SYSTEM; RANKING; RECOMMENDER SYSTEMS;

EID: 67649933212     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (32)

References (18)
  • 2
    • 24644461564 scopus 로고    scopus 로고
    • Automatic text summarization based on word-clusters and ranking algorithms
    • Proceedings of the 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, SPAin, March 21-23, Springer
    • Amini, M.-R., Usunier, N., and Gallinari, P. (2005). Automatic text summarization based on word-clusters and ranking algorithms. In Proceedings of the 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, SPAin, March 21-23, Lecture Notes in Computer Science, pages 142-156. Springer.
    • (2005) Lecture Notes in Computer Science , pp. 142-156
    • Amini, M.-R.1    Usunier, N.2    Gallinari, P.3
  • 3
    • 27844439373 scopus 로고    scopus 로고
    • Ando and Zhang (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research.
    • Ando and Zhang (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research.
  • 9
    • 33746919833 scopus 로고    scopus 로고
    • Generalized nonnega-tive matrix approximations with bregman divergences
    • Dhillon, I. S. and Sra, S. (2006). Generalized nonnega-tive matrix approximations with bregman divergences. NIPS.
    • (2006) NIPS
    • Dhillon, I.S.1    Sra, S.2
  • 12
    • 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
  • 13
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • Lee, D. D. and Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature.
    • (1999) Nature
    • Lee, D.D.1    Seung, H.S.2
  • 18
    • 67649963200 scopus 로고    scopus 로고
    • Srebro, N., Rennie, J. D. M., and Jaakkola, T. S. (2004). Maximum-margin matrix factorization. In Saul, L. K., Weiss, Y, and Bottou, 1., editors, Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA.
    • Srebro, N., Rennie, J. D. M., and Jaakkola, T. S. (2004). Maximum-margin matrix factorization. In Saul, L. K., Weiss, Y, and Bottou, 1., editors, Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.