Trading off Relevance and Revenue in the Jobs Marketplace: Estimation, Optimization and Auction Design
This addresses the tradeoff between revenue and relevance for job marketplace platforms, presenting incremental improvements in auction design.
The paper tackles the problem of position allocation in job marketplaces to balance revenue from promoted jobs and relevance for seekers, demonstrating that incorporating seeker preferences and position-aware auctions can improve relevance with minimal revenue impact.
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.