Hybrid attacks on model-based social recommender systems

Junliang Yu, Min Gao, Wenge Rong, Wentao Li, Qingyu Xiong and Junhao Wen

Physica A: Statistical Mechanics and its Applications, 2017, vol. 483, issue C, 171-181

Abstract: With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

Keywords: Social recommender system; Shilling attack; Hybrid attack; Matrix factorization (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:483:y:2017:i:c:p:171-181

DOI: 10.1016/j.physa.2017.04.048

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