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

Farzad Pourbabaee, Sophie Yanying Sheng, Peter McCrory, Luke Simon, Di Mo

TL;DR

This paper analyzes position allocation in job marketplaces, addressing the tension between short-term monetization and long-term seeker engagement. It develops two complementary approaches to improve relevance with minimal revenue loss: incorporating seeker preferences through eRelevance via a Seeker-Weight parameter, and implementing position-aware auctions that adjust scores by position. The authors provide a causal-estimation framework with a parsimonious Rel model and a dynamic optimization formulation to balance immediate gains with future engagement, complemented by a dynamic-programming/RL approach to learn optimal weights. They compare GFP and VCG-based, position-aware allocations through simulations, showing that GFP tends to maximize revenue while VCG enhances relevance, highlighting a practical tradeoff and a path for improving multi-slot auction design in two-sided marketplaces.

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.

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

TL;DR

This paper analyzes position allocation in job marketplaces, addressing the tension between short-term monetization and long-term seeker engagement. It develops two complementary approaches to improve relevance with minimal revenue loss: incorporating seeker preferences through eRelevance via a Seeker-Weight parameter, and implementing position-aware auctions that adjust scores by position. The authors provide a causal-estimation framework with a parsimonious Rel model and a dynamic optimization formulation to balance immediate gains with future engagement, complemented by a dynamic-programming/RL approach to learn optimal weights. They compare GFP and VCG-based, position-aware allocations through simulations, showing that GFP tends to maximize revenue while VCG enhances relevance, highlighting a practical tradeoff and a path for improving multi-slot auction design in two-sided marketplaces.

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.

Paper Structure

This paper contains 11 sections, 10 equations, 3 figures.

Table of Contents

  1. Abstract
  2. 1. Introduction
  3. 2. Relevance-Revenue Tradeoff
  4. 2.1 Causal Estimation
  5. Parsimonious Model:
  6. 2.2 Dynamic Optimization
  7. 3. Auction Design
  8. 3.1 Position-Unaware and Generalized First-Price Ranking
  9. 3.2 Position-Aware and Bipartite Matching
  10. 3.3 Simulations
  11. 4. Concluding Remarks

Figures (3)

  • Figure 1: Dispersion in relevance metric in experiment
  • Figure 2: Average revenue per seeker vs. # of jobs
  • Figure 3: Average relevance per seeker vs. # of jobs