A recommender system is used to help users sort through the vast amount of data they encounter on a daily basis. The systems are built on algorithms, which analyze the users and identify their characteristics and preferences in order to create personalized recommendations. Consequently, a recommender system helps capture the users' interest and attention. This is especially important in streaming services where users easily lose patience. Previous research has put a lot of emphasis on the technical aspect of recommender systems, such as how to improve the accuracy of the algorithms. This allows for further research investigating the recommender systems from a user perspective. The purpose of this paper was to contribute insights about users' experiences of recommender systems in digital services. This study focused solely on Netflix and how their users experience the various dimensions of the recommender system. Semi-structured interviews have been conducted with ten experienced Netflix users. The interviews have been analyzed using a thematic analysis, where five themes were identified. Based on the results of the analysis along with the previous research, several conclusions were reached. The study shows the desire for further control over the recommendations, in the form of customizable solutions in the recommender. The results also show the need for higher transparency, as the users described a lack of explanations for why certain recommendations were made. Finally, the study shows that it is challenging to find the right amount of variation in the system as the preference of variation is highly individual.