# Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design

arXiv: https://arxiv.org/abs/2504.03618 · HTML: https://mlsanity.com/paper/2504.03618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [New Auction Algorithms for the Assignment Problem and Extensions](https://mlsanity.com/paper/2310.03159/) (2023)
- [The Ad Types Problem](https://mlsanity.com/paper/1907.04400/) (2019)

## Abstract (arXiv)

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.