Shilling attack detection for collaborative recommender systems: a gradient boosting method

Shilling attack detection for collaborative recommender systems: a gradient boosting method

Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific securit...

Full description

Bibliographic Details
Main Authors: Chen Shao, Yue zhong yi Sun
Format: Article
Language:English
Published: AIMS Press 2022-05-01
Series:Mathematical Biosciences and Engineering
Subjects:
recommendation system
collaborative filtering
shilling attack detection
ensemble learning
gradient boosting
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2022342?viewType=HTML

Internet

https://www.aimspress.com/article/doi/10.3934/mbe.2022342?viewType=HTML

Similar Items