Abstract
With the rapid development of e-commerce, the security issues of recommender systems have been widely investigated. Malicious users can benefit from injecting great quantities of fake profiles into recommender systems to reduce the frequency of undesired recommendation items. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Although a multitude of studies have been devoted to shilling attack modeling and detection, few of them focus on group shilling attack. The attackers in a shilling group work together to manipulate the output of the recommender system. Based on the model of the loose version of Group Shilling Attack Generation Algorithm (GSAGenl), we design an anti-similarity group shilling attack model (AGSA). AGSA rationalizes the evaluation time interval of the group attack and strengthens the destructive powers of the group shilling attacks.
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Acknowledgment
This work was supported by the National Natural Science Foundation of P. R. China (Nos. 61672297, 61572260 and 61373138), the Key Research and Development Program of Jiangsu Province (Social Development Program, Nos. BE2016185 and BE2016177), Postdoctoral Foundation (Nos. 2015M570468 and 2016T90485), The Sixth Talent Peaks Project of Jiangsu Province (No. DZXX-017), Jiangsu Natural Science Foundation for Excellent Young Scholar (No. BK20160089), the Fund of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201516).
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Wang, P., Qi, L., Huang, H., Li, F., Yu, C. (2017). AGSA: Anti-similarity Group Shilling Attacks. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_10
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DOI: https://doi.org/10.1007/978-981-10-6442-5_10
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