Nowadays, recommender systems are useful tools to filter items and information for users. There is a huge diversity of approaches to create customized recommendations. Because of this, a developer needs to know the features of these approaches to select which one is the best approach in a specific domain. In this paper, we explain the first step in the design of our intelligent framework to create recommender systems. This first step is called RecOnto: an ontology to model recommender system as a collection of components related between them. This ontology defines and classifies all components that compound a recommender system. Moreover, depending on the information used by the recommender system, it can filter the components used by the system. In addition, this ontology can be extended to add more components or apply this model in other domains. Finally, we explain an example about how to apply RecOnto to model CoCARE, a real context-aware recommender system in the health domain.