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Volumn , Issue , 2012, Pages 67-74

On top-k recommendation using social networks

Author keywords

Matrix factorization; Recommender system; Social network

Indexed keywords

COLD START; COMPREHENSIVE STUDIES; DATA SETS; FEATURE SPACE; HIT RATIO; MATRIX FACTORIZATIONS; NEAREST NEIGHBORS; OBJECTIVE FUNCTIONS; RECOMMENDATION ACCURACY; RECOMMENDATION METHODS; ROOT MEAN SQUARE ERRORS; SOCIAL NETWORKS; TRUST NETWORKS; TRUST RELATIONSHIP;

EID: 84867381268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2365952.2365969     Document Type: Conference Paper
Times cited : (150)

References (25)
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    • DOI 10.1109/TKDE.2005.99
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    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.6 , pp. 734-749
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  • 7
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    • A matrix factorization technique with trust propagation for recommendation in social networks
    • M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In ACM Conference on Recommender Systems (RecSys), 2010.
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    • Jamali, M.1    Ester, M.2
  • 9
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    • (2008) ACM Conference on Knowledge Discovery and Data Mining , pp. 426-434
    • Koren, Y.1
  • 20
    • 57949113756 scopus 로고    scopus 로고
    • Improving regularized singular value decomposition for collaborative filtering
    • A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In KDDCup, 2007.
    • (2007) KDDCup
    • Paterek, A.1
  • 24
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    • Training and testing of recommender systems on data missing not at random
    • H. Steck. Training and testing of recommender systems on data missing not at random. In ACM Conference on Knowledge Discovery and Data Mining, pages 713-22, 2010.
    • (2010) ACM Conference on Knowledge Discovery and Data Mining , pp. 713-722
    • Steck, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.