Many of our daily activities and decisions are driven by algorithms. This is particularly evident in our interactions with contemporary cultural content, where recommender algorithms deal with most of their access, production, and distribution. In this context, the Algorithmic Experience (AX) design framework emerged to guide the design of users’ experiences with algorithms for social media platforms. However, thus far, a framework to design specifically for AX within the context of movie recommender algorithms was lacking. To this end, the present study combines a semiotic inspection analysis of the Netflix interface with sensitized design workshops and semi-structured interviews to explore AX requirements for movie recommender algorithms. Linking the analysis with design opportunities, we shed light on AX suited for movie recommender systems. Moreover, we extend the current AX design framework with two new design areas: algorithmic usefulness and algorithmic social practices.