优化招聘平台职位排序，平衡推荐相关性与平台收入。
Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design
- 结合求职者偏好调整排序，提升匹配相关性。
- 采用位置感知拍卖机制，在不损收入前提下提升推荐质量。
- 适合关注平台长期健康与广告收益的算法设计者。
我们研究招聘平台中的职位排序问题，即平台如何为每位求职者确定职位展示顺序。排序机制的设计对市场效率至关重要，它同时影响短期推广职位带来的收入和长期求职者参与度。本文聚焦于收入与相关性之间的权衡，以及职位拍卖机制的创新。我们展示了两种在几乎不影响收入的前提下提升相关性的方法：一是融合求职者偏好信息，二是采用位置感知的拍卖机制。
原文摘要 · Abstract (English)
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.
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