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Volumn 1226, Issue , 2014, Pages 34-44

Improved questionnaire trees for active learning in recommender systems

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; FORESTRY; KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; SOCIAL NETWORKING (ONLINE); SURVEYS; TREES (MATHEMATICS);

EID: 84925011348     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (2)

References (9)
  • 1
    • 20844435854 scopus 로고    scopus 로고
    • Toward the next generation of recommender systems: A survey of the state-of-The-art and possible extensions
    • G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-The-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749, 2005.
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.6 , pp. 734-749
    • Adomavicius, G.1    Tuzhilin, A.2
  • 2
    • 79952423377 scopus 로고    scopus 로고
    • Adaptive bootstrapping of recommender systems using decision trees
    • ACM
    • N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In WSDM, pages 595-604. ACM, 2011.
    • (2011) WSDM , pp. 595-604
    • Golbandi, N.1    Koren, Y.2    Lempel, R.3
  • 3
    • 57349112337 scopus 로고    scopus 로고
    • Personalized active learning for collaborative filtering
    • ACM
    • A. S. Harpale and Y. Yang. Personalized active learning for collaborative filtering. In SIGIR, pages 91-98. ACM, 2008.
    • (2008) SIGIR , pp. 91-98
    • Harpale, A.S.1    Yang, Y.2
  • 4
    • 79951737281 scopus 로고    scopus 로고
    • A bayesian approach toward active learning for collaborative filtering
    • R. Jin and L. Si. A bayesian approach toward active learning for collaborative filtering. In UAI, 2004.
    • (2004) UAI
    • Jin, R.1    Si, L.2
  • 6
    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42:30-37, 2009.
    • (2009) Computer , vol.42 , pp. 30-37
    • Koren, Y.1    Bell, R.2    Volinsky, C.3
  • 7
    • 77954605272 scopus 로고    scopus 로고
    • Learning preferences of new users in recommender systems: An information theoretic approach
    • Dec
    • A. M. Rashid, G. Karypis, and J. Riedl. Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explor. Newsl., 10(2):90-100, Dec. 2008.
    • (2008) SIGKDD Explor. Newsl. , vol.10 , Issue.2 , pp. 90-100
    • Rashid, A.M.1    Karypis, G.2    Riedl, J.3
  • 8
    • 63449105336 scopus 로고    scopus 로고
    • Online-updating regularized kernel matrix fac-torization models for large-scale recommender systems
    • ACM
    • S. Rendle and L. Schmidt-Thieme. Online-updating regularized kernel matrix fac-torization models for large-scale recommender systems. In ACM Conference on Recommender Systems (RecSys), pages 251-258. ACM, 2008.
    • (2008) ACM Conference on Recommender Systems (RecSys , pp. 251-258
    • Rendle, S.1    Schmidt-Thieme, L.2
  • 9
    • 80052119372 scopus 로고    scopus 로고
    • Functional matrix factorizations for cold-start recommendation
    • ACM
    • K. Zhou, S.-H. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. SIGIR '11, pages 315-324. ACM, 2011.
    • (2011) SIGIR '11 , pp. 315-324
    • Zhou, K.1    Yang, S.-H.2    Zha, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.