Human-Computer Interaction (HCI) is undergoing a paradigm shift toward placing well-being at the center of technology development. Information systems (IS) research is called upon to align business goals with different well-being orientations, particularly those of hedonic and eudaimonic individuals. While hedonic-oriented individuals choose relaxation and pleasure as their goals, eudaimonic-oriented individuals are interested in meaningful experiences that go beyond pleasure. Surprisingly, there is a lack of datasets provided for recommender systems (RS) considering well-being orientations. We developed and thus present the first public dataset in this paper that was created based on an online survey with 229 participants. Respondents were asked to name their favorite movies and books and then the participants were clustered into two orientations using the HEMA-Revised (HEMA-R) Scale. In total, we collected 1,563 items (799 movies and 764 books). The dataset is intended to contribute to the improvement of (group) RS architectures.