DeepFair: Deep Learning for Improving Fairness in Recommender Systems-Reference-Cited by-同舟云学术

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DeepFair: Deep Learning for Improving Fairness in Recommender Systems

Published:2021 Issue:6 Volume:6 Page:86
ISSN:1989-1660
Container-title:International Journal of Interactive Multimedia and Artificial Intelligence
language:en
Short-container-title:IJIMAI

Author:

Bobadilla Jesús,Lara-Cabrera Raúl,González-Prieto Ángel,Ortega Fernando

Publisher

Universidad Internacional de La Rioja

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Computer Vision and Pattern Recognition,Statistics and Probability,Signal Processing

Cited by 28 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. Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation;Artificial Intelligence Review;2024-03-29

2. Wasserstein GAN-based architecture to generate collaborative filtering synthetic datasets;Applied Intelligence;2024-02

3. Generating and Testing Synthetic Datasets for Recommender Systems to Improve Fairness in Collaborative Filtering Research;2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA);2023-12-04

4. Creating synthetic datasets for collaborative filtering recommender systems using generative adversarial networks;Knowledge-Based Systems;2023-11

5. Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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