Recommendation attack detection based on improved Meta Pseudo Labels

Abstract

Attackers attempt to bias the outputs of collaborative recommender systems by maliciously rating goods or services. To detect such attacks, many deep learning-based detection methods have been proposed and shown to be feasible. However, most methods require a large number of labeled user profiles for training to ensure good detection performance. To address this issue, in this paper, we propose a deep semisupervised detection approach based on the improved Meta Pseudo Labels, named DSSD-ImMPL. DSSD-ImMPL can achieve high detection performance given a small number of labeled training samples and a certain number of unlabeled training samples. We first improve the Meta Pseudo Labels method by generating a group of student networks by an experienced teacher network instead of only one student network in the original Meta Pseudo Labels method to improve the classification performance. Then, we use the group of student networks to detect the recommendation attack. The detection performance is verified with classical, mixed, GSA-GANs, and real attacks on three benchmark datasets by comparing DSSD-ImMPL with the state-of-the-art detection methods.