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Enhancing the robustness of recommender systems against spammers

  • Published:2018-11-01 Issue:11 Volume:13 Page:e0206458
  • ISSN:1932-6203
  • Container-title:PLOS ONE
  • language:en
  • Short-container-title:PLoS ONE

Author:

Zhang Chengjun,Liu Jin,Qu Yanzhen,Han Tianqi,Ge Xujun,Zeng AnORCID

Funder

Natural Science Foundation of Jiangsu Province of China

the National Natural Science Foundation of China

the Natural Science Foundation of Beijing

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Cited by 6 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. Predicting popularity of online products via collective recommendations;Physica A: Statistical Mechanics and its Applications;2024-05

2. Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review;IEEE Access;2024

3. An in-depth analysis of robustness and accuracy of recommendation systems;2023 IEEE International Conference on Data Mining Workshops (ICDMW);2023-12-04

4. Modeling and Optimization of Discrete Evolutionary Systems of İnformation Security Management in a Random Environment;Machine Learning and Autonomous Systems;2022

5. Exploratory Study of Machine Learning Algorithms in Recommender Systems;Proceedings of International Conference on Advanced Computing Applications;2021-11-24
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