Recommender systems developers are constantly faced with difficult design decisions. Additionally, the number of options that a recommender systems developer has to consider continually grows over time with new innovations. The machine learning community is in a similar situation and has come together to tackle the problem. They invented concepts and tools to make machine learning development both easier and faster. These developments are categorized as automated machine learning (AutoML). As a result, the AutoML community formed and continuously innovates new approaches. Inspired by AutoML, the recommender systems community has recently understood the need for automation and sparsely introduced AutoRecSys. The goal of AutoRecSys is not to replace recommender systems developers but to improve performance through the automation of design decisions. With AutoRecSys, recommender systems engineers do not have to focus on easy but time-consuming tasks and are free to pursue difficult engineering tasks instead. Additionally, AutoRecSys enables easier access to recommender systems for beginners as it reduces the amount of knowledge required to get started with the development of recommender systems. AutoRecSys, like AutoML, is still early in its development and does not yet cover the whole development pipeline. Additionally, it is not yet clear, under which circumstances AutoML approaches can be transferred to recommender systems. Our research intends to close this gap by improving AutoRecSys both with regard to the transfer of AutoML and novel approaches. Furthermore, we focus specifically on the development of novel automation approaches for data processing and training. We note that the realization of AutoRecSys is going to be a community effort. Our part in this effort is to research AutoRecSys fundamentals, build practical tools for the community, raise awareness of the advantages of automation, and catalyze AutoRecSys development.