Personalizing Marketplace Policies with Competing Objectives and Constrained Experiments: Evidence from a Job Marketplace
arXiv:2606.30932v1 Announce Type: new Abstract: Two-sided marketplaces connect distinct user groups whose interests often conflict -- improving outcomes on one side could degrade the other side's experience. To address this challenge, we deploy an integrated framework for personalizing free-value thresholds -- a policy governing the scope of complimentary services for job listings -- across a two-sided job marketplace connecting millions of employers and job seekers. Our personalized policy delivers statistically significant and economically sizable lift in the target metric while respecting e
This research is emerging as AI techniques advance sufficiently to implement complex personalized policy frameworks within large-scale, real-world systems like job marketplaces.
It demonstrates a practical, data-driven approach to optimizing multi-sided platforms, offering a blueprint for enhancing efficiency and user experience in various digital marketplaces.
Marketplace operators can move beyond one-size-fits-all policies to dynamic, personalized solutions that improve outcomes for distinct user groups, even with competing objectives.
- · Two-sided marketplace operators
- · Data scientists and AI platform developers
- · Employers using optimized job marketplaces
- · Job seekers on platforms with better matching
- · Marketplaces slow to adopt AI personalization
- · Legacy policy setting methods
- · Users disadvantaged by undifferentiated policy frameworks
Personalized policies lead to improved platform efficiency and user satisfaction on two-sided marketplaces.
Increased efficiency translates to higher revenue and user retention for platforms, potentially increasing their market dominance.
The success of such sophisticated AI applications could accelerate adoption across other complex economic systems, impacting regulation and economic models.
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Read at arXiv cs.LG