Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908301660258304 |
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| author | Pourbabaee, Farzad Sheng, Sophie Yanying McCrory, Peter Simon, Luke Mo, Di |
| author_facet | Pourbabaee, Farzad Sheng, Sophie Yanying McCrory, Peter Simon, Luke Mo, Di |
| contents | We study the problem of position allocation in job marketplaces, where the platform determines the ranking of the jobs for each seeker. The design of ranking mechanisms is critical to marketplace efficiency, as it influences both short-term revenue from promoted job placements and long-term health through sustained seeker engagement. Our analysis focuses on the tradeoff between revenue and relevance, as well as the innovations in job auction design. We demonstrated two ways to improve relevance with minimal impact on revenue: incorporating the seekers preferences and applying position-aware auctions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03618 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design Pourbabaee, Farzad Sheng, Sophie Yanying McCrory, Peter Simon, Luke Mo, Di Computer Science and Game Theory Machine Learning We study the problem of position allocation in job marketplaces, where the platform determines the ranking of the jobs for each seeker. The design of ranking mechanisms is critical to marketplace efficiency, as it influences both short-term revenue from promoted job placements and long-term health through sustained seeker engagement. Our analysis focuses on the tradeoff between revenue and relevance, as well as the innovations in job auction design. We demonstrated two ways to improve relevance with minimal impact on revenue: incorporating the seekers preferences and applying position-aware auctions. |
| title | Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design |
| topic | Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2504.03618 |