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Volumn Part F128815, Issue , 2013, Pages 659-667

FISM: Factored item similarity models for Top-N recommender systems

Author keywords

Item similarity; Recommender systems; Sparse data; Topn

Indexed keywords

DATA MINING; RECOMMENDER SYSTEMS;

EID: 85006130680     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2487575.2487589     Document Type: Conference Paper
Times cited : (765)

References (13)
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    • D. Saad, editor, Cambridge University Press, Cambridge, UK, 1998. revised, oct
    • L. Bottou. Online algorithms and stochastic approximations. In D. Saad, editor, Online Learning and Neural Networks. Cambridge University Press, Cambridge, UK, 1998. revised, oct 2012.
    • (2012) Online Learning and Neural Networks
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    • Improving regularized singular value decomposition for collaborative filtering
    • A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop, volume 2007, pages 5-8, 2007.
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    • Paterek, A.1
  • 9
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    • Content-based recommendation systems
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    • Pazzani, M.1    Billsus, D.2
  • 12
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    • Recommender systems handbook
    • Springer Science+ Business Media, LLC, 2011, 1
    • F. Ricci, L. Rokach, B. Shapira, and P. Kantor. Recommender systems handbook. Recommender Systems Handbook:, ISBN 978-0-387-85819-7. Springer Science+ Business Media, LLC, 2011, 1, 2011.
    • (2011) Recommender Systems Handbook
    • Ricci, F.1    Rokach, L.2    Shapira, B.3    Kantor, P.4
  • 13
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Series B (Methodological
    • R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), pages 267-288, 1996.
    • (1996) Journal of the Royal Statistical Society , pp. 267-288
    • Tibshirani, R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.