Policy personalization in labor markets with conflicting objectives

In two-sided digital markets, such as job platforms, the challenge of balancing conflicting interests between employers and candidates is constant. Improving the experience on one side can harm the other, requiring personalization strategies that consider multiple objectives simultaneously. An innovative approach involves dynamically adjusting the thresholds for free services on job postings, using ensemble models that separately predict the impact on target metrics and constraint metrics. This technique reduces the risk of negatively affecting the non-prioritized side by more than 10% compared to single-objective optimization methods. Additionally, it is necessary to extrapolate treatment effects from limited experiments to untested levels, assuming empirically validated monotonicity. The production implementation confirms the accuracy of the extrapolations and compliance with engagement limits. This type of solution, which integrates artificial intelligence, data analysis, and optimization, is key for companies operating complex platforms. At Q2BSTUDIO, we develop custom applications that incorporate machine learning models and customized business logic, helping organizations make data-driven decisions. We combine AI for businesses with AWS and Azure cloud services to scale these architectures securely and efficiently. Furthermore, our cybersecurity capabilities ensure the protection of sensitive data, while business intelligence services with Power BI allow visualizing the impact of policies. AI agents automate continuous adjustment processes, facilitating adaptation to market changes. All of this is part of a custom software ecosystem that transforms the management of platforms with conflicting objectives.