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.

翻译：双边市场体现了激励机制的异质性：生产者追求曝光度而消费者追求相关性，通过约束优化平衡这些竞争性目标已成为标准实践。然而真实平台面临更细粒度的复杂性：消费者在偏好和参与模式上存在差异，生产者在目录价值和产能方面各不相同，而商业目标还会施加超越原始相关性的额外约束。我们在这些现实条件下形式化了双边公平性，将先前研究从软性单物品分配扩展到离散多物品推荐。我们引入条件风险价值作为消费者侧目标函数，以压缩群体层面的效用差异，并将商业约束直接整合到优化过程中。实验表明，在生产者约束不产生消费者成本的"免费公平"机制在多物品场景中不复存在。引人注目的是，适度的公平性约束能通过将曝光从饱和生产者转向多样化来改善商业指标。可扩展求解器能以极小的时间成本匹配精确解，使得公平感知分配在大规模场景中具备可行性。这些发现重新定义了公平性——它并非平台效率的代价，而是维持市场健康可持续发展的调节杠杆。