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Volumn , Issue , 2010, Pages 341-344

Active learning driven by Rating Impact Analysis

Author keywords

Active learning; Impact analysis; Recommender systems

Indexed keywords

COLLABORATIVE FILTERING; FORECASTING; RECOMMENDER SYSTEMS;

EID: 78649973151     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1864708.1864782     Document Type: Conference Paper
Times cited : (14)

References (10)
  • 1
    • 20844435854 scopus 로고    scopus 로고
    • Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions
    • Adomavicius, G. and Tuzhilin, A. 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 (2005), 734-749.
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.6 , pp. 734-749
    • Adomavicius, G.1    Tuzhilin, A.2
  • 6
    • 79952391729 scopus 로고    scopus 로고
    • Why does collaborative filtering work? Transaction-based recommendation model validation and selection by analyzing bipartite random graphs
    • ijoc.1100.0385. DOI=http://doi.acm.org/10.1287/ijoc.1100.0385
    • Huang, Z. and Zeng, D.D. Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs. INFORMS JOURNAL ON COMPUTING, (2010), ijoc.1100.0385. DOI= http://doi.acm.org/10.1287/ijoc.1100.0385.
    • (2010) INFORMS JOURNAL on COMPUTING
    • Huang, Z.1    Zeng, D.D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.