Recommender systems (RSs) are thus powerful tools to support end-users in their respective sectors. However, the development process is challenging since it requires domain customization, which brings inefficiencies and, therefore, more development time. This work presents the development for my PhD thesis of a methodology for leveraging Model-Driven Engineering (MDE) and model-weaving techniques to facilitate the development of easily configurable RSs. This approach is intended for reuse enhancement, error reduction, and time-to-deploy reduction. The current achieved results underline the gap in the current literature and the lack of configurable support systems that RS providers need, pointing out the need for future research to develop methodologies that can treat dynamic and context-aware data effectively.