# Fairness and Diversity in Recommender Systems: A Survey

arXiv: https://arxiv.org/abs/2307.04644 · HTML: https://mlsanity.com/paper/2307.04644/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2023.

## Built on by (most cited first, 22 of 22)

- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Pluralistic Off-policy Evaluation and Alignment](https://mlsanity.com/paper/2509.19333/) (2025)
- [FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness](https://mlsanity.com/paper/2504.07801/) (2025)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System](https://mlsanity.com/paper/2507.02000/) (2025)
- [LLMs as Orchestrators: Constraint-Compliant Multi-Agent Optimization for Recommendation Systems](https://mlsanity.com/paper/2601.19121/) (2026)
- [Representative Ranking for Deliberation in the Public Sphere](https://mlsanity.com/paper/2503.18962/) (2025)
- [Offline A/B Testing of Slate Recommendation Systems with LLMs: Reducing the Dependency on Pre-Collected User Interaction Data](https://mlsanity.com/paper/2511.04541/) (2025)
- [Measuring Individual User Fairness with User Similarity and Effectiveness Disparity](https://mlsanity.com/paper/2602.02516/) (2026)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems](https://mlsanity.com/paper/2603.11399/) (2026)
- [Enhancing Math Learning in an LMS Using AI-Driven Question Recommendations](https://mlsanity.com/paper/2504.14098/) (2025)
- [Looking for Fairness in Recommender Systems](https://mlsanity.com/paper/2507.12242/) (2025)
- [PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems](https://mlsanity.com/paper/2507.19067/) (2025)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)
- [Contrastive Learning for Diversity-Aware Product Recommendations in Retail](https://mlsanity.com/paper/2602.08886/) (2026)
- [Aligning Recommendations with User Popularity Preferences](https://mlsanity.com/paper/2604.01036/) (2026)
- [Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty](https://mlsanity.com/paper/2604.16419/) (2026)
- [Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders](https://mlsanity.com/paper/2608.08284/) (2026)
- [Toward Collective-Centric Evaluation of Preference Inference for Participatory Democracy](https://mlsanity.com/paper/2609.02990/) (2026)
- [Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation](https://mlsanity.com/paper/2609.15094/) (2026)

## Abstract (arXiv)

Recommender systems are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals proves to be inadequate in addressing real-world concerns, leading to increasing attention to fairness-aware and diversity-aware recommender systems. While most existing studies explore fairness and diversity independently, we identify strong connections between these two domains. In this survey, we first discuss each of them individually and then dive into their connections. Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level. With this expanded perspective on user and item-level diversity, we re-interpret fairness studies from the viewpoint of diversity. This fresh perspective enhances our understanding of fairness-related work and paves the way for potential future research directions. Papers discussed in this survey along with public code links are available at https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems .