The purpose of recommenders is often summarized as "help the users find relevant items", and the predominant opera- tionalization of this goal has been to focus on the ability to numerically estimate the users' preferences for unseen items or to provide users with item lists ranked in accordance to the estimated preferences. This dominant, albeit narrow, view of the recommendation problem has been tremendously helpful in advancing research in diferent ways, e.g., through the establishment of standardized evaluation procedures and metrics. In reality, recommender systems can serve a vari- ety of purposes from the point of view of both consumers and providers. Most of the purposes, however, are signif- icantly underexplored, even though many of them are ar- guably more aligned with the real-world expectations for rec- ommenders than our current predominant paradigm. There- fore, it is important to revisit our conceptualizations of the potential goals of recommenders and their operationalization as research problems. In this paper, we discuss a framework of recommendation goals and purposes and highlight pos- sible future directions and challenges related to the opera- tionalization of such alternative problem formulations.