Abstract
The recommendation systems have become an important tool to solve the problem of information overload. However, the recommendation system is greatly fragile as it relies heavily on behavior data of users. It is very easy for a host of malicious merchants to inject shilling attacks in order to control the recommendation results. Some papers on shilling attack have proposed the detection methods, but they ignored experimental performance of injecting a small number of attacks and time overhead. To solve above issues, we propose a novel detection method of shilling attack based on T-distribution over dynamic time intervals. Firstly, we proposed Dynamic Time Intervals to divide the rating history of items into multiple time windows; secondly, the T-distribution is employed to calculate the similarity between windows, and the feature of T-distribution is obvious to detect small samples; thirdly, abnormal windows are identified by analyzing the T value, time difference and rating actions quantity of each window; fourthly, abnormal rating actions are detected by analyzing rating mean of abnormal windows. Extensive experiments are conducted. Comparing with similar shilling detection approaches, the experimental results demonstrate the effectiveness of the proposed method.
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Acknowledgment
This work is supported by the National Nature Science Foundation of China (91646117, 61702368) and Natural Science Foundation of Tianjin (17JCYBJC15200, 18JCQNJC00700).
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Yuan, W., Xiao, Y., Jiao, X., Sun, C., Zheng, W., Wang, H. (2020). A Novel Shilling Attack Detection Method Based on T-Distribution over the Dynamic Time Intervals. In: Nah, Y., Kim, C., Kim, SY., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020 International Workshops. DASFAA 2020. Lecture Notes in Computer Science(), vol 12115. Springer, Cham. https://doi.org/10.1007/978-3-030-59413-8_19
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