Cook, Code, Conquer: Machine Learning Applications in Food Recommendation and Sports

Ondertitel:Machine Learning Applications in Food Recommendation and Sports

Korte inhoud:While a good amount of Machine Learning research relies on controlled "toy datasets," real-world data and applications are significantly more unpredictable, introducing unique challenges in variability, scalability, and practical implementation. This thesis examines the practical applications of Machine Learning in real-life scenarios, focusing on personalized recipe recommendations and sports analytics. By addressing these complexities, it bridges the gap between theoretical advancements and practical deployment, providing insights and solutions for real-world use cases. In the retail domain, a graph-based recommendation system models relationships between users, recipes, and ingredients to deliver personalized, actionable suggestions. The system addresses issues such as popularity bias and the cold-start problem, achieving a 40% improvement in accuracy over traditional methods. The framework also enhances diversity, reduces bias, and provides explainability by identifying key features driving recommendations, offering a practical solution for improving user engagement and sales. In sports, Machine Learning techniques predict cycling race outcomes using a learn-to-rank approach. This novel application explains predictions using SHAP values, highlighting race-specific factors that influence performance. These insights provide a tool for pre-race analysis and data-driven strategy development. Future enhancements, including probabilistic models and simulations, could extend predictions across seasons and multi-day events. A data-driven analysis of professional cycling identifies the age of peak performance across athlete specializations, including sprinters, all-rounders, and general classification riders. By combining classification and clustering techniques, the research offers actionable insights for athlete career planning. Personalized models could refine these findings further by tailoring predictions to individual trajectories. The development of a low-cost, real-time player tracking system for field hockey demonstrates the potential of affordable technology to democratize sports analytics. Using a simple GoPro setup, the system achieves 93.8% accuracy in tracking players, providing underrepresented sports with access to data-driven insights. Further refinements could improve tracking performance, especially in high-traffic scenarios. By addressing real-world challenges and proposing future advancements, this research highlights the transformative potential of Machine Learning in retail and sports. From improving recommendations to optimizing athletic performance and engagement, it offers a practical roadmap for deploying advanced algorithms in diverse, impactful applications.

Jaar van publicatie:2025

Trefwoorden:Computer. Automation

Toegankelijkheid:Open