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Volumn 910, Issue , 2012, Pages 30-32

Modeling difficulty in recommender systems

Author keywords

Dificulty; Evaluation; Recommender systems; User modeling

Indexed keywords

DIFICULTY; EVALUATION; EVALUATION MEASURES; USER MODELING;

EID: 84891943849     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (9)

References (12)
  • 1
    • 20844435854 scopus 로고    scopus 로고
    • Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
    • G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transaction on Knowledge and Data Engineering, 17(6):734-749, 2005.
    • (2005) IEEE Transaction on Knowledge and Data Engineering , vol.17 , Issue.6 , pp. 734-749
    • Adomavicius, G.1    Tuzhilin, A.2
  • 3
    • 57349146373 scopus 로고    scopus 로고
    • Lessons from the netix prize challenge
    • R. M. Bell and Y. Koren. Lessons from the netix prize challenge. SIGKDD Explor. Newsl., 9(2):75-79, 2007.
    • (2007) SIGKDD Explor. Newsl. , vol.9 , Issue.2 , pp. 75-79
    • Bell, R.M.1    Koren, Y.2
  • 9
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • L. Kuncheva and C. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51:181-207, 2003.
    • (2003) Machine Learning , vol.51 , pp. 181-207
    • Kuncheva, L.1    Whitaker, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.