[Paper Review] Recommendation Fairness: From Static to Dynamic

Dell Zhang, Jun Wang|arXiv (Cornell University)|Sep 5, 2021

Advanced Bandit Algorithms Research80 references4 citations

TL;DR

This paper advocates for a paradigm shift in recommendation fairness from static evaluation to dynamic, reinforcement learning (RL)-based control, proposing that fairness be embedded within dynamic, long-term decision-making frameworks. It introduces the integration of fairness into RL-based recommenders using multi-agent and multi-objective optimization within stochastic games, enabling non-stop, adaptive fairness monitoring and control.

ABSTRACT

Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.

Motivation & Objective

• Address the limitations of static fairness evaluation in recommender systems by shifting toward dynamic, long-term fairness control.
• Integrate fairness constraints into reinforcement learning frameworks to optimize both user satisfaction and equitable outcomes over time.
• Explore multi-agent and multi-objective optimization to model complex fairness trade-offs in interactive recommendation environments.
• Advance the state of the art in responsible AI by embedding fairness into the core of dynamic decision-making processes in recommender systems.
• Position fairness as an evolving, continuous optimization problem rather than a one-time intervention or static metric.

Proposed method

• Model recommendation as a Markov Decision Process (MDP), where user states evolve over time and actions are item recommendations.
• Employ reinforcement learning (RL) algorithms—such as contextual bandits, DQN, policy gradients, and actor-critic methods—to learn policies that maximize long-term user reward.
• Incorporate fairness constraints into the RL objective function using techniques like fairness-aware reward shaping and constrained RL.
• Extend the framework to stochastic games to model interactions among multiple agents (e.g., users, systems, or content providers) with competing or cooperative fairness goals.
• Apply multi-objective optimization (Pareto optimization) to balance competing objectives such as personalization, diversity, and fairness.
• Utilize offline RL methods to train fairness-aware policies using historical interaction data without costly online exploration.

Experimental results

Research questions

• RQ1 How can fairness in recommender systems be effectively modeled as a dynamic, long-term optimization problem rather than a static evaluation?
• RQ2 What are the key challenges in integrating fairness constraints into reinforcement learning-based recommendation systems?
• RQ3 How can multi-agent and multi-objective optimization frameworks improve fairness in interactive, evolving recommendation environments?
• RQ4 In what ways can simulation-based optimization enhance the robustness and adaptability of fairness-aware RL policies?
• RQ5 What role do stochastic games play in modeling complex fairness dynamics among multiple stakeholders in recommendation systems?

Key findings

• The shift from static fairness evaluation to dynamic, RL-based control enables continuous fairness monitoring and adaptive policy updates.
• Integrating fairness into RL allows for long-term optimization of user satisfaction while mitigating biases related to demographics, popularity, and user activeness.
• Multi-agent and multi-objective optimization frameworks provide a scalable and principled way to model fairness trade-offs in complex recommendation ecosystems.
• Simulation-based optimization and offline RL enable the training of fairness-aware policies without live user feedback, reducing deployment risks.
• Stochastic games offer a natural formalism for modeling fairness in environments with multiple interacting agents, each with distinct fairness and performance objectives.
• The proposed framework supports non-stop, real-time fairness control, aligning with the dynamic nature of modern recommender systems.