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Browsing by Author "GASPAR, DIOGO FRANCISCO SOUSA"

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  • Otimização do algoritmo de correspondência de prestadores de serviços para uma plataforma de serviços domésticos
    Publication . GASPAR, DIOGO FRANCISCO SOUSA; Malheiro, Nuno Filipe Teixeira
    This thesis addresses the optimization of the service provider–client matching algorithm in Oscar, a growing start-up offering on-demand and scheduled home services. The existing algorithm relied on rules with a low flexibility, resulting in high cancellation rates, poor provider engagement, and limited visibility into decision-making. To overcome these challenges, the work combines two complementary directions: algorithmic improvement through a fairnessaware boosting framework, and the integration of observability to enhance monitoring and transparency. A systematic mapping study was conducted to analyze state-of-the-art matching algorithms, supply–demand compatibility strategies, and observability methodologies. Based on the findings, a Conceptual Boosting Framework was designed, incorporating modular heuristics such as task performance and recent activity boosts, while ensuring configurability and fairness monitoring. Observability was integrated via structured logging, telemetry, and tracing, enabling detailed insights into algorithmic decisions and operational metrics. The solution was implemented and deployed in the production system of Oscar. Evaluation included unit and integration tests, as well as one-week of A/B testing comparing the new algorithm against the baseline. The final results demonstrated significant improvements: cancellations decreased by 17.9%, pool time by 18.5%, and time-to-accept by 26.1%, while acceptance rate increased by 15.3%. These outcomes validate the effectiveness of the approach in improving efficiency and user satisfaction. The thesis contributes by introducing a practical framework that balances efficiency, fairness, and observability, empirically validating it on a live service platform, and documenting a design that is extensible to other gig-economy domains. Limitations include low prioritization for testing, reliance on heuristics rather than machine learning, and lack of quantitative fairness auditing. Future work should address these aspects through long-term experiments, machine learning-based adaptive boosting, and formal fairness evaluation.
    2025-10-17Master thesis Embargoed access Show more