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Volumn 37, Issue 8, 2010, Pages 5666-5672

Unified collaborative filtering model based on combination of latent features

Author keywords

Classifier; Collaborative filtering; Latent feature; Probabilistic latent semantic analysis; Recommender System

Indexed keywords

COLLABORATIVE FILTERING; COMPACT MODEL; CONSTANT TIME; EXISTING METHOD; EXPERIMENTAL EVALUATION; EXTERNAL FEATURES; MAPPING SCHEME; PROBABILISTIC LATENT SEMANTIC ANALYSIS; RECOMMENDER SYSTEMS; TEXT ANALYSIS; UNIFIED METHOD;

EID: 77951205209     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.044     Document Type: Article
Times cited : (41)

References (16)
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    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , pp. 734-749
    • Adomavicius, G.1    Tuzhilin, A.2
  • 3
    • 0001024110 scopus 로고
    • First and second order methods for learning: Between steepest descent and Newton's method
    • Battiti R. First and second order methods for learning: Between steepest descent and Newton's method. Neural Computation 4 (1992) 141-166
    • (1992) Neural Computation , vol.4 , pp. 141-166
    • Battiti, R.1
  • 9
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • Hofmann T. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42 (2001) 177-196
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    • Hofmann, T.1
  • 10
    • 3042742744 scopus 로고    scopus 로고
    • Latent semantic models for collaborative filtering
    • Hofmann T. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22 (2004) 89-115
    • (2004) ACM Transactions on Information Systems , vol.22 , pp. 89-115
    • Hofmann, T.1
  • 11
    • 37049012047 scopus 로고    scopus 로고
    • Does a one-size recommendation system fit all? The effectiveness of collaborative filtering based recommendation systems across different domains and search modes
    • Im I., Hars A., and GmbH I. Does a one-size recommendation system fit all? The effectiveness of collaborative filtering based recommendation systems across different domains and search modes. ACM Transactions on Information Systems 26 (2007) 1-30
    • (2007) ACM Transactions on Information Systems , vol.26 , pp. 1-30
    • Im, I.1    Hars, A.2    GmbH, I.3
  • 12
    • 38349041037 scopus 로고    scopus 로고
    • Collaborative filtering on streaming data with interest-drifting
    • Li X., Barajas J.M., and Ding Y. Collaborative filtering on streaming data with interest-drifting. Intelligent Data Analysis 11 (2007) 75-87
    • (2007) Intelligent Data Analysis , vol.11 , pp. 75-87
    • Li, X.1    Barajas, J.M.2    Ding, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.