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13 December 2021 Shilling detection algorithm based on non-negative user embedding matrix factorization
Ying Xu, Lixin Han, Jun Zhu
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Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871L (2021) https://doi.org/10.1117/12.2624915
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China
Abstract
With the wide application of collaborative filtering recommendation systems, more and more attackers disrupt the recommendation ranking to benefit from the manipulated recommendation results. Therefore, how to effectively detect torrent attacks becomes more and more critical. To solve the problems of low extraction of user information and low utilization of user implicit features in the existing shilling attack detection algorithms, a shilling attack detection algorithm based on non-negative user embedding matrix factorization is proposed. Firstly, the user-user co-occurrence matrix is constructed by using the pointwise mutual information. Secondly, it is found that there is a linear correlation between the quality of the user-user co-occurrence matrix before dimensionality reduction and the quality of the user implicit features. The centralized similarity is used to construct the user deep similarity matrix before dimensionality reduction, to improve the quality of the user implicit features. Furthermore, non-negative matrix factorization is used to extract the features of users in the deep similarity matrix. Through the verification in movielens100k data set and Amazon data set, it is found that the improved algorithm can detect torrent attacks more effectively.
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Ying Xu, Lixin Han, and Jun Zhu "Shilling detection algorithm based on non-negative user embedding matrix factorization", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871L (13 December 2021); https://doi.org/10.1117/12.2624915
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KEYWORDS
Detection and tracking algorithms
Machine learning
Data modeling
Feature extraction
Nuclear weapons
Photovoltaics
Information security
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