Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering-Reference-Cited by-同舟云学术

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Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering

Published:2020-10 Issue:5 Volume:7 Page:1189-1199
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Zhang Fuzhi^ORCID,Wang Shilei

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/9253503/09169810.pdf?arnumber=9169810

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel clustered-based detection method for shilling attack in private environments;PeerJ Computer Science;2024-06-24

2. Intrusion Detection Model for Wireless Sensor Networks Based on FedAvg and XGBoost Algorithm;International Journal of Distributed Sensor Networks;2024-03-25

3. ROBUREC: Building a Robust Recommender using Autoencoders with Anomaly Detection;Proceedings of the International Conference on Advances in Social Networks Analysis and Mining;2023-11-06

4. Recommendation attack detection based on improved Meta Pseudo Labels;Knowledge-Based Systems;2023-11

5. Detecting malicious reviews and users affecting social reviewing systems: A survey;Computers & Security;2023-10

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