# Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning

arXiv: https://arxiv.org/abs/2201.00140 · HTML: https://mlsanity.com/paper/2201.00140/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2022.

## Built on by (most cited first, 29 of 29)

- [Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation](https://mlsanity.com/paper/2305.07609/) (2023)
- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond](https://mlsanity.com/paper/2501.10945/) (2025)
- [Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness](https://mlsanity.com/paper/2310.19691/) (2023)
- [Two-Stage Constrained Actor-Critic for Short Video Recommendation](https://mlsanity.com/paper/2302.01680/) (2023)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Interactive Recommendation Agent with Active User Commands](https://mlsanity.com/paper/2509.21317/) (2025)
- [Intersectional Two-sided Fairness in Recommendation](https://mlsanity.com/paper/2402.02816/) (2024)
- [Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2308.02935/) (2023)
- [Evaluating Bias in Spoken Dialogue LLMs for Real-World Decisions and Recommendations](https://mlsanity.com/paper/2510.02352/) (2025)
- [Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning](https://mlsanity.com/paper/2306.03293/) (2023)
- [Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)](https://mlsanity.com/paper/2208.11083/) (2022)
- [Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2405.03082/) (2024)
- [Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems](https://mlsanity.com/paper/2407.16828/) (2024)
- [When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making](https://mlsanity.com/paper/2603.16673/) (2026)
- [Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation](https://mlsanity.com/paper/2306.12165/) (2023)
- [Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework](https://mlsanity.com/paper/2307.11379/) (2023)
- [Impression-Aware Recommender Systems](https://mlsanity.com/paper/2308.07857/) (2023)
- [MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels](https://mlsanity.com/paper/2401.12593/) (2024)
- [Alleviating User-Sensitive bias with Fair Generative Sequential Recommendation Model](https://mlsanity.com/paper/2506.19777/) (2025)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation](https://mlsanity.com/paper/2606.24042/) (2026)
- [Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings](https://mlsanity.com/paper/2402.14305/) (2024)
- [Fair Resource Allocation in Weakly Coupled Markov Decision Processes](https://mlsanity.com/paper/2411.09804/) (2024)
- [KLAN: Kuaishou Landing-page Adaptive Navigator](https://mlsanity.com/paper/2507.23459/) (2025)
- [EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning](https://mlsanity.com/paper/2510.19893/) (2025)
- [Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking](https://mlsanity.com/paper/2606.26369/) (2026)

## Abstract (arXiv)

The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizing the main recommendation objective, e.g., CTR. While this alleviates the impact of unfair recommendations, the expected return of an approach may significantly compromise the recommendation accuracy due to the inherent trade-off between fairness and utility. This motivates us to deal with these conflicting objectives and explore the optimal trade-off between them in recommendation. One conspicuous approach is to seek a Pareto efficient solution to guarantee optimal compromises between utility and fairness. Moreover, considering the needs of real-world e-commerce platforms, it would be more desirable if we can generalize the whole Pareto Frontier, so that the decision-makers can specify any preference of one objective over another based on their current business needs. Therefore, in this work, we propose a fairness-aware recommendation framework using multi-objective reinforcement learning, called MoFIR, which is able to learn a single parametric representation for optimal recommendation policies over the space of all possible preferences. Specially, we modify traditional DDPG by introducing conditioned network into it, which conditions the networks directly on these preferences and outputs Q-value-vectors. Experiments on several real-world recommendation datasets verify the superiority of our framework on both fairness metrics and recommendation measures when compared with all other baselines. We also extract the approximate Pareto Frontier on real-world datasets generated by MoFIR and compare to state-of-the-art fairness methods.