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

2025-04-04

AI Summary

This paper addresses the fundamental trade-off between relevance and advertising revenue in job ranking on recruitment platforms. We propose a joint preference-aware ranking and position-aware auction mechanism. Methodologically, we integrate causal inference estimation, multi-objective optimization, and mechanism design theory to construct a real-time deployable ranking–auction co-optimization framework that preserves short-term platform revenue while enhancing long-term matching quality. Our key contribution lies in dynamically coupling job seeker preference modeling with ad-slot value estimation, enabling Pareto-improving relevance gains under strict revenue constraints. Empirical results demonstrate that, with less than 1% advertising revenue loss, click-through rate and application conversion rate increase by over 12%, while user retention and overall platform health significantly improve.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Algorithm Configuration
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges

Abstract

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.

Problem

• Balancing job relevance and platform revenue optimization
• Designing ranking mechanisms for marketplace efficiency
• Improving relevance with minimal revenue impact

Innovation

• Incorporating seeker preferences for relevance
• Applying position-aware auction designs
• Balancing revenue and relevance tradeoffs