Ruoqi Liu developed a machine learning-driven evaluation framework for recommender systems within the HealthRex/CDSS repository. Leveraging Python, RecBole, and MLOps practices, Ruoqi designed the system to support data splitting, synthetic data generation, and evaluation of models such as BPR and SASRec. The framework introduced configurable metrics, including Recall, Precision, and NDCG, enabling rigorous model comparison and reproducibility. By focusing on modularity and extensibility, Ruoqi’s work addressed the need for faster validation cycles and reliable benchmarking in health recommender systems. The depth of the implementation established a robust foundation for ongoing experimentation and model assessment within the project.