Abstract
The robustness of recommender systems has drawn recently more and more attention of both industry and academia. 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. Meanwhile, since the rating profiles in a shilling group are carefully designed, it is hard to detect them by traditional methods. This paper presents a generative model to create shilling group in which every pair of attackers has high diversity. In particular, both strict and loose versions of group shilling attack generation algorithm are proposed. Experimental results on MovieLens data set demonstrate that the shilling group generated by the our model can not only exert large negative effect to recommender systems, but also avoid the detection by the traditional methods.
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Wang, Y., Wu, Z., Cao, J., Fang, C. (2012). Towards a Tricksy Group Shilling Attack Model against Recommender Systems. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_56
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DOI: https://doi.org/10.1007/978-3-642-35527-1_56
Publisher Name: Springer, Berlin, Heidelberg
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