For a long time, recommender systems presented their results in the form of simple item lists. In recent years, however, multi-list interfaces have become the de-facto standard in industry, presenting users with numerous collections of recommendations, one below the other, each containing items with common characteristics. Netflix's interface, for instance, shows movies from certain genres, new releases, and lists of curated content. Spotify recommends new songs and albums, podcasts on specific topics, and what similar users are listening to. Despite their popularity, research on these so-called “carousels” is still limited. Few authors have investigated how to simulate the user behavior and how to optimize the recommendation process accordingly. The number of studies involving users is even smaller, with sometimes conflicting results. Consequently, little is known about how to design carousel-based interfaces for achieving the best user experience. This mini review aims to organize the existing knowledge and outlines directions that may improve the multi-list presentation of recommendations in the future.