关键词: 协同过滤, 托攻击, 托攻击检测, 深度稀疏自动编码器, 项目时间流行度等级

Abstract: In collaborative filtering-based recommender systems, malicious users can bias the systems’ recommendation output by injecting a large number of fake profiles, and then achieve the purpose of attack. To detect shilling attacks, some researchers extract the features of attack profiles from different views, which are mainly based on the users’ratings or the hypothesis that attacks are concentrated in short time. However, the performance of feature extraction-based detection methods usually relies on the quality of artificial feature engineering. Moreover, the detection features are not universal in different environments, and the feature extraction requires high knowledge costs. To address these problems, this paper focuses on the user temporal preferences to the rated items, and proposes an ensemble detection method for shilling attacks based on deep sparse autoencoder. Firstly, the item popularity is set to several grades in different time intervals based on the wavelet transform, and the ratings are preprocessed to obtain the user-item temporal popularity grade matrix. Secondly, the deep sparse autoencoder is used to automatically extract the features from user-item temporal popularity grade matrix, which can obtain the low level feature expressions for the user rating patterns and eliminate the artificial feature engineering. Finally, as base classifier, SVM is used to detect the attacks based on the features of each layer in deep sparse autoencoder, and then the final detection result is generated by voting the detection results of each layer. Experimental results on the Netflix dataset indicate that the proposed method has better detection performance under average attack, AoP attack, shifting attack, power item attack, and power user attack.

Key words: collaborative filtering, shilling attacks, shilling attack detection, deep sparse autoencoder, item temporal popularity grade