DeepFair: Deep Learning for Improving Fairness in Recommender Systems
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Abstract (arXiv)
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 1; 2025: 1; 2026: 2.
12 papers.
- 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providerscited by 31
- 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
- 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
- 2025Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender Systemcited by 5
- 2022Mitigating Algorithmic Bias with Limited Annotationscited by 2
- 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluationcited by 2
- 2023Deep Context Interest Network for Click-Through Rate Predictioncited by 2
- 2023Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networkscited by 1
- 2021Deep Variational Models for Collaborative Filtering-based Recommender Systemscited by 0
- 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systemscited by 0
- 2026From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Reviewcited by 0
- 2026Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendationcited by 0
- 2026Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation12 Mar 2026
- 2026From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review25 Feb 2026
- 2025Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System1 Jul 2025
- 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systems3 May 2024
- 2023Deep Context Interest Network for Click-Through Rate Prediction11 Aug 2023
- 2023Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation16 May 2023
- 2023Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks2 Mar 2023
- 2022Mitigating Algorithmic Bias with Limited Annotations20 Jul 2022
- 2022Fairness in Recommendation: Foundations, Methods and Applications26 May 2022
- 2022Fairness in Recommender Systems: Research Landscape and Future Directions23 May 2022
- 2021Deep Variational Models for Collaborative Filtering-based Recommender Systems27 Jul 2021
- 2021TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers19 Apr 2021