Contemporary users (players, consumers) of digital games have thousands of products to choose from, which makes finding games that fit their interests challenging. Towards addressing this challenge, in this paper two different formulations of Archetypal Analysis for Top-L recommender tasks using implicit feedback are presented: factor- and neighborhood-oriented models. These form the first application of recommender systems to digital games. Both models are tested on a dataset of 500,000 users of the game distribution platform Steam, covering game ownership and playtime data across more than 3000 games. Compared to four other recommender models (nearest neighbor, two popularity models, random baseline), the archetype based models provide the highest recall rates showing that Archetypal Analysis can be successfully applied for Top-L recommendation purposes.