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 practical platforms face interacting sources of heterogeneity that are often studied separately: multi-item recommendation, heterogeneous consumer groups, and business constraints beyond raw relevance. In this work, we present and study offline optimization framework for analyzing these trade-offs in an unified manner, extending prior two-sided formulations to represent more realistic discrete multi-item recommendations. Within this framework, we couple producer-side exposure guarantees with a consumer-group fairness objective and explicit business-oriented constraints. Our experiments show that the previously reported ``free fairness'' regime from highly stylized single-item recommendation settings disappears once each consumer receives multiple recommendations, and that moderate producer-fairness constraints can improve simulated business metrics by diversifying exposure away from saturated producers. We further show that reduction of inter-group disparity, preserves competitive overall utility.
From: Dominykas Šeputis [view email]
[v1]
Wed, 11 Feb 2026 10:59:32 UTC (4,898 KB)
[v2]
Thu, 12 Feb 2026 18:47:28 UTC (4,898 KB)
[v3]
Thu, 2 Jul 2026 12:03:32 UTC (4,897 KB)
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