Analyzing the Efficiency of Recommender Systems Using Machine Learning

RedEMC is an e-learning web platform that utilizes a hybrid recommender system to suggest learning resources and online courses to Latin American doctors and other health specialists in the context of Continuing Medical Education. Explicit and implicit feedback were collected in a period of six months to determine the usefulness of personalized recommendations through predictive models, using machine learning approaches and methods. The main contribution of this research is to show how to utilize the feedback given by students who received personalized recommendations in a real study case to create predictive machine learning models that assist organizations to analyze the efficiency of their recommender systems. Once predictive models are generated, educational institutions and companies could also utilize them to make strategic decisions regarding the accomplishment of their organizational goals.