Affiliations: [a] College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China | [b] Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province | [c] Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, China | [d] School of Electronic and Information Engineering, Foshan University, Foshan, Guangdong, China | [e] Independent Researcher, Hong Kong, China
Correspondence:
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Corresponding author: Patrick P.K. Chan, Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, China. E-mail: patrickchan@scut.edu.cn.
Abstract: A recommender system is susceptible to manipulation through the injection of carefully crafted profiles. Some recent profile identification methods only perform well in specific attack scenarios. A general attack detection method is usually complicated or requires label samples. Such methods are prone to overtraining easily, and the process of annotation incurs high expenses. This study proposes an unsupervised divide-and-conquer method aiming to identify attack profiles, utilizing a specifically designed model for each kind of shilling attack. Initially, our method categorizes the profile set into two attack types, namely Standard and Obfuscated Behavior Attacks. Subsequently, profiles are separated into clusters within the extracted feature space based on the identified attack type. The selection of attack profiles is then determined through target item analysis within the suspected cluster. Notably, our method offers the advantage of requiring no prior knowledge or annotation. Furthermore, the precision is heightened as the identification method is designed to a specific attack type, employing a less complicated model. The outstanding performance of our model, validated through experimental results on MovieLens-100K and Netflix under various attack settings, demonstrates superior accuracy and reduced running time compared to current detection methods in identifying Standard and Obfuscated Behavior Attacks.
