Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets
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| Format: | Preprint |
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2026
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| _version_ | 1866912900121100288 |
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| author | Seputis, Dominykas Timans, Alexander Verma, Rajeev |
| author_facet | Seputis, Dominykas Timans, Alexander Verma, Rajeev |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_10739 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets Seputis, Dominykas Timans, Alexander Verma, Rajeev Computer Science and Game Theory Information Retrieval 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. |
| title | Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets |
| topic | Computer Science and Game Theory Information Retrieval |
| url | https://arxiv.org/abs/2602.10739 |