Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets

2026-02-11

AI Summary

This work addresses the trade-off between fairness and efficiency in multi-item recommendation within heterogeneous two-sided markets. The authors propose an optimization framework that jointly accounts for disparities in consumer group utility, heterogeneity in producer capabilities, and platform business constraints. Innovatively integrating Conditional Value-at-Risk (CVaR) into consumer-side fairness modeling, the study extends formal fairness notions from soft single-item allocations to discrete multi-item recommendations. The resulting combinatorial optimization model embeds CVaR-based fairness objectives alongside practical business constraints and is paired with an efficient, scalable solver. Experimental results demonstrate that lossless fairness is unattainable in multi-item settings; however, moderate fairness constraints can enhance business metrics by diversifying exposure, while the proposed solver significantly reduces runtime without compromising solution accuracy.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Two-sided marketplaces embody heterogeneity in incentives: producers seek exposure while consumers seek relevance, and balancing these competing objectives through constrained optimization is now a standard practice. Yet real platforms face finer-grained complexity: consumers differ in preferences and engagement patterns, producers vary in catalog value and capacity, and business objectives impose additional constraints beyond raw relevance. We formalize two-sided fairness under these realistic conditions, extending prior work from soft single-item allocations to discrete multi-item recommendations. We introduce Conditional Value-at-Risk (CVaR) as a consumer-side objective that compresses group-level utility disparities, and integrate business constraints directly into the optimization. Our experiments reveal that the"free fairness"regime, where producer constraints impose no consumer cost, disappears in multi item settings. Strikingly, moderate fairness constraints can improve business metrics by diversifying exposure away from saturated producers. Scalable solvers match exact solutions at a fraction of the runtime, making fairness-aware allocation practical at scale. These findings reframe fairness not as a tax on platform efficiency but as a lever for sustainable marketplace health.

Problem

• two-sided markets
• fairness
• recommendation
• heterogeneity
• multi-item allocation

Innovation

• two-sided fairness
• Conditional Value-at-Risk (CVaR)
• multi-item recommendation
• constrained optimization
• scalable solver