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| Autori principali: | , , , , |
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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2602.19689 |
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| _version_ | 1866912920438308864 |
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| author | Sekiya, Kazuki Otani, Suguru Komatsu, Yuki Ohkawa, Sachio Noda, Shunya |
| author_facet | Sekiya, Kazuki Otani, Suguru Komatsu, Yuki Ohkawa, Sachio Noda, Shunya |
| contents | Two-sided platforms must recommend users to users, where matches (termed \emph{dates} in this paper) require mutual interest and activity on both sides. Naive ranking by predicted dating probabilities concentrates exposure on a small subset of highly responsive users, generating congestion and overstating efficiency. We model recommendation as a many-to-many matching problem and design integrators that map predicted login, like, and reciprocation probabilities into recommendations under attention constraints. We introduce \emph{effective dates}, a congestion-adjusted metric that discounts matches involving overloaded receivers. We then propose \emph{exposure-constrained deferred acceptance} (ECDA), which limits receiver exposure in terms of expected likes or dates rather than headcount. Using production-grade predictions from a large Japanese dating platform, we show in calibrated simulations that ECDA increases effective dates and receiver-side dating probability despite reducing total dates. A large-scale regional field experiment confirms these effects in practice, indicating that exposure control improves equity and early-stage matching efficiency without harming downstream engagement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19689 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Integrating Predictive Models into Two-Sided Recommendations: A Matching-Theoretic Approach Sekiya, Kazuki Otani, Suguru Komatsu, Yuki Ohkawa, Sachio Noda, Shunya General Economics Economics Two-sided platforms must recommend users to users, where matches (termed \emph{dates} in this paper) require mutual interest and activity on both sides. Naive ranking by predicted dating probabilities concentrates exposure on a small subset of highly responsive users, generating congestion and overstating efficiency. We model recommendation as a many-to-many matching problem and design integrators that map predicted login, like, and reciprocation probabilities into recommendations under attention constraints. We introduce \emph{effective dates}, a congestion-adjusted metric that discounts matches involving overloaded receivers. We then propose \emph{exposure-constrained deferred acceptance} (ECDA), which limits receiver exposure in terms of expected likes or dates rather than headcount. Using production-grade predictions from a large Japanese dating platform, we show in calibrated simulations that ECDA increases effective dates and receiver-side dating probability despite reducing total dates. A large-scale regional field experiment confirms these effects in practice, indicating that exposure control improves equity and early-stage matching efficiency without harming downstream engagement. |
| title | Integrating Predictive Models into Two-Sided Recommendations: A Matching-Theoretic Approach |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2602.19689 |