메뉴 건너뛰기




Volumn , Issue , 2013, Pages 273-280

Efficient top-N recommendation for very large scale binary rated datasets

Author keywords

Collaborative filtering; Implicit feedback; Million Song Dataset challenge; Top N recommendation

Indexed keywords

COLLABORATIVE FILTERING ALGORITHMS; EXPLICIT FEEDBACK; IMPLICIT FEEDBACK; MILLION SONG DATASET CHALLENGE; SCALABLE ALGORITHMS; SCORING FUNCTIONS; SIMILARITY MATRIX; TOP-N RECOMMENDATION;

EID: 84887580616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2507157.2507189     Document Type: Conference Paper
Times cited : (103)

References (15)
  • 1
    • 84887591836 scopus 로고    scopus 로고
    • A preliminary study on a recommender system for the million songs dataset challenge
    • F. Aiolli. A preliminary study on a recommender system for the million songs dataset challenge. In IIR, pages 73-83, 2013.
    • (2013) IIR , pp. 73-83
    • Aiolli, F.1
  • 5
    • 3042821101 scopus 로고    scopus 로고
    • Item-based top-n recommendation algorithms
    • M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143-177, 2004.
    • (2004) ACM Trans. Inf. Syst. , vol.22 , Issue.1 , pp. 143-177
    • Deshpande, M.1    Karypis, G.2
  • 6
    • 81055135842 scopus 로고    scopus 로고
    • A comprehensive survey of neighborhood-based recommendation methods
    • C. Desrosiers and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, pages 107-144. 2011.
    • (2011) Recommender Systems Handbook , pp. 107-144
    • Desrosiers, C.1    Karypis, G.2
  • 7
    • 67049164166 scopus 로고    scopus 로고
    • Collaborative filtering for implicit feedback datasets
    • Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, pages 263-272, 2008.
    • (2008) ICDM , pp. 263-272
    • Hu, Y.1    Koren, Y.2    Volinsky, C.3
  • 8
    • 0035747556 scopus 로고    scopus 로고
    • Evaluation of item-based top-n recommendation algorithms
    • G. Karypis. Evaluation of item-based top-n recommendation algorithms. In CIKM, pages 247-254, 2001.
    • (2001) CIKM , pp. 247-254
    • Karypis, G.1
  • 12
    • 85052617391 scopus 로고    scopus 로고
    • Item-based collaborative filtering recommendation algorithms
    • B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285-295, 2001.
    • (2001) WWW , pp. 285-295
    • Sarwar, B.M.1    Karypis, G.2    Konstan, J.A.3    Riedl, J.4
  • 14
    • 84889587149 scopus 로고    scopus 로고
    • Collaborative ranking with 17 parameters
    • P. Bartlett, F. Pereira, C. Burges, L. Bottou, and K. Weinberger, editors
    • M. Volkovs and R. Zemel. Collaborative ranking with 17 parameters. In P. Bartlett, F. Pereira, C. Burges, L. Bottou, and K. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 2303-2311. 2012.
    • (2012) Advances in Neural Information Processing Systems , vol.25 , pp. 2303-2311
    • Volkovs, M.1    Zemel, R.2
  • 15
    • 33745872861 scopus 로고    scopus 로고
    • A user-item relevance model for log-based collaborative filtering
    • M. Lalmas, A. MacFarlane, S. M. RÃijger, A. Tombros, T. Tsikrika, and A. Yavlinsky, editors, volume 3936 of Lecture Notes in Computer Science, Springer
    • J. Wang, A. P. de Vries, and M. J. T. Reinders. A user-item relevance model for log-based collaborative filtering. In M. Lalmas, A. MacFarlane, S. M. RÃijger, A. Tombros, T. Tsikrika, and A. Yavlinsky, editors, ECIR, volume 3936 of Lecture Notes in Computer Science, pages 37-48. Springer, 2006.
    • (2006) ECIR , pp. 37-48
    • Wang, J.1    De Vries, A.P.2    Reinders, M.J.T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.