Recommender systems have become essential in domains like streaming services, social media platforms, and e-commerce websites. However, the development of a recommender system involves a complex pipeline with preprocessing, data splitting, algorithm and model selection, and postprocessing stages, requiring critical design decisions. Every stage of the recommender systems pipeline requires design decisions that influence the performance of the recommender system. To ease design decisions, automated machine learning (AutoML) techniques have been adapted to the field of recommender systems, resulting in various AutoRecSys libraries. Nevertheless, these libraries lack library independence and limit flexibility in integrating automation techniques from different sources. In response, our research aims to enhance the usability of AutoML techniques for design decisions in recommender system pipelines. We focus on developing flexible and library-independent automation techniques for algorithm selection, model selection, and postprocessing steps. By enabling developers to make informed choices and ease the recommender system development process, we decrease the developer’s effort while improving the performance of the recommender systems. Moreover, we want to analyze the cost-to-benefit ratio of automation techniques in recommender systems, evaluating the computational overhead and the resulting improvements in predictive performance. Our objective is to leverage AutoML concepts to automate design decisions in recommender system pipelines, reduce manual effort, and enhance the overall performance and usability of recommender systems.