Shilling attacks and security of recommender systems

doi:10.3969/j.issn.1000-1565.2018.06.013

Abstract

Abstract: As an effective solution for solving the problem of information overload, collaborative recommender systems have been widely applied to many fields. However, the occurrence of shilling attacks has seriously affected the quality of recommendation. Therefore, how to protect recommender systems against shilling attacks has become hotspot issue in recommender systems. This paper first introduces the motivation of shilling attacks and features of various attack models, and then analyzes the state of the art in shilling attacks detection techniques and robust collaborative recommendation algorithms in detail. Finally, the future development and research directions are discussed.

Key words: recommender systems, collaborative filtering, shilling attacks, shilling attack detection, robust recommendation

CLC Number:

TP393

TIAN Junfeng, CAI Hongyun. Shilling attacks and security of recommender systems[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(6): 640-647.

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