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Volumn 2006, Issue , 2006, Pages 542-547

Classification features for attack detection in collaborative recommender systems

Author keywords

Attack detection; Collaborative filtering; Recommender systems; Robustness

Indexed keywords

COMPUTER CRIME; COMPUTER SUPPORTED COOPERATIVE WORK; FEATURE EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

EID: 33749548497     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1150402.1150465     Document Type: Conference Paper
Times cited : (232)

References (13)
  • 7
    • 33749544444 scopus 로고    scopus 로고
    • Effective attack models for shilling item-based collaborative filtering systems
    • Chicago, IL, August
    • B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Effective attack models for shilling item-based collaborative filtering systems. In Proc. of the 2005 WebKDD Workshop, Chicago, IL, August 2005.
    • (2005) Proc. of the 2005 WebKDD Workshop
    • Mobasher, B.1    Burke, R.2    Bhaumik, R.3    Williams, C.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.