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Volumn , Issue , 2010, Pages 71-78

Fast ALS-based matrix factorization for explicit and implicit feedback datasets

Author keywords

Alternating least squares; Collaborative filtering; Com putational complexity; Implicit and explicit feedback; Matrix factorization; Ridge regression

Indexed keywords

ALTERNATING LEAST SQUARES; COLLABORATIVE FILTERING; EXPLICIT FEEDBACK; MATRIX FACTORIZATIONS; RIDGE REGRESSION;

EID: 78649966572     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1864708.1864726     Document Type: Conference Paper
Times cited : (158)

References (13)
  • 1
    • 36849079891 scopus 로고    scopus 로고
    • Modeling relationships at multiple scales to improve accuracy of large recommender systems
    • New York, NY, USA. ACM
    • R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD-07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 95-104, New York, NY, USA, 2007. ACM.
    • (2007) KDD-07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining , pp. 95-104
    • Bell, R.1    Koren, Y.2    Volinsky, C.3
  • 2
    • 49749086487 scopus 로고    scopus 로고
    • Scalable collaborative filtering with jointly derived neighborhood interpolation weights
    • Omaha, Nebraska, USA
    • R. M. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM-07, 7th IEEE Int. Conf. on Data Mining, pages 43-52, Omaha, Nebraska, USA, 2007.
    • (2007) ICDM-07, 7th IEEE Int. Conf. on Data Mining , pp. 43-52
    • Bell, R.M.1    Koren, Y.2
  • 6
    • 3042829247 scopus 로고    scopus 로고
    • An empirical analysis of design choices in neighborhood-based collaborative flltering algorithms
    • J. Herlocker, J. A. Konstan, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative flltering algorithms. Inf. Retr., 5(4):287-310, 2002.
    • (2002) Inf. Retr. , vol.5 , Issue.4 , pp. 287-310
    • Herlocker, J.1    Konstan, J.A.2    Riedl, J.3
  • 8
    • 65449121157 scopus 로고    scopus 로고
    • Factorization meets the neighborhood: A multifaceted collaborative flltering model
    • New York, NY, USA
    • Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative flltering model. In KDD-08: 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 426-434, New York, NY, USA, 2008.
    • (2008) KDD-08: 13th ACM Int. Conf. on Knowledge Discovery and Data Mining , pp. 426-434
    • Koren, Y.1
  • 9
    • 57949113756 scopus 로고    scopus 로고
    • Improving regularized singular value decomposition for collaborative flltering
    • San Jose, California, USA
    • A. Paterek. Improving regularized singular value decomposition for collaborative flltering. In KDD Cup Workshop at KDD-07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 39-42, San Jose, California, USA, 2007.
    • (2007) th ACM Int. Conf. on Knowledge Discovery and Data Mining , pp. 39-42
    • Paterek, A.1
  • 12
    • 77951114011 scopus 로고    scopus 로고
    • Investigation of various matrix factorization methods for large recommender systems
    • Las Vegas, NV, USA, August 24
    • G. Takács, I. Pilászy, B. Németh, and D. Tikk. Investigation of various matrix factorization methods for large recommender systems. In 2nd Netflix-KDD Workshop at KDD-08: 14th ACM Int. Conf. on Knowledge Discovery and Data Mining, Las Vegas, NV, USA, August 24, 2008.
    • (2008) th ACM Int. Conf. on Knowledge Discovery and Data Mining
    • Takács, G.1    Pilászy, I.2    Németh, B.3    Tikk, D.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.