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Volumn , Issue , 2005, Pages 612-617

A maximum entropy web recommendation system: Combining collaborative and content features

Author keywords

Maximum Entropy; Recommendation; User Profiling; Web Usage Mining

Indexed keywords

DATABASE SYSTEMS; ENTROPY; NUMERICAL ANALYSIS; SEMANTICS; USER INTERFACES;

EID: 32344441911     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1081870.1081945     Document Type: Conference Paper
Times cited : (99)

References (18)
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    • J. Goodman. Sequential conditional generalized iterative scaling. In Proceedings of NAACL-2002, 2002.
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    • Goodman, J.1
  • 8
    • 84958652227 scopus 로고    scopus 로고
    • Integrating web usage and content mining for more effective personalization
    • Lecture Notes in Computer Science (LNCS) 1875. Springer, September
    • B. Mobasher, H. Dai, T. Luo, Y. Sun, and J. Zhu. Integrating web usage and content mining for more effective personalization. In E-Commerce and Web Technologies: Proceedings of the EC- WEB 2000 Conference, Lecture Notes in Computer Science (LNCS) 1875, pages 165-176. Springer, September 2000.
    • (2000) E-commerce and Web Technologies: Proceedings of the EC- WEB 2000 Conference , pp. 165-176
    • Mobasher, B.1    Dai, H.2    Luo, T.3    Sun, Y.4    Zhu, J.5
  • 13
    • 0033325071 scopus 로고    scopus 로고
    • A framework for collaborative, content-based and demographic filtering
    • M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393-408, 1999.
    • (1999) Artificial Intelligence Review , vol.13 , Issue.5-6 , pp. 393-408
    • Pazzani, M.1
  • 14
    • 0012253296 scopus 로고    scopus 로고
    • Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments
    • Seattle, WA
    • A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of 17th UAI, Seattle, WA, 2001.
    • (2001) Proceedings of 17th UAI
    • Popescul, A.1    Ungar, L.2    Pennock, D.3    Lawrence, S.4
  • 17
    • 2342586046 scopus 로고    scopus 로고
    • Collaborative ensembling learning: Combining collaborative and content-based information filtering
    • K. Yu, A. Schwaighofer, V. Tresp, W. Ma, and H. Zhang. Collaborative ensembling learning: Combining collaborative and content-based information filtering. In Proceedings of 19th UAI, 2003.
    • (2003) Proceedings of 19th UAI
    • Yu, K.1    Schwaighofer, A.2    Tresp, V.3    Ma, W.4    Zhang, H.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.