.png){kind=icon}
返回
Recommendation attack detection based on improved Meta Pseudo Labels
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2023.110931.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Collaborative recommender systems
Recommendation attack
Attack detection
Meta Pseudo Labels
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems推荐系统中基于SVM和目标项分析的svm-tia先令攻击检测方法.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender SystemsBs-sc: 一种在协作推荐系统中检测先令配置文件的无监督方法
PREAI
分享收藏βP: A novel approach to filter out malicious rating profiles from recommender systemsΒ p: 一种从推荐系统中过滤出恶意评级配置文件的新方法
PREAI
分享收藏
查看更多内容