  Streaming services use recommender systems to surface the right music to
users. Playlists are a popular way to present music in a list-like fashion, ie
as a plain list of songs. An alternative are tours, where the songs alternate
segues, which explain the connections between consecutive songs. Tours address
the user need of seeking background information about songs, and are found to
be superior to playlists, given the right user context. In this work, we
provide, for the first time, a user-centered evaluation of two tour-generation
algorithms (Greedy and Optimal) using semi-structured interviews. We assess the
algorithms, we discuss attributes of the tours that the algorithms produce, we
identify which attributes are desirable and which are not, and we enumerate
several possible improvements to the algorithms, along with practical
suggestions on how to implement the improvements. Our main findings are that
Greedy generates more likeable tours than Optimal, and that three important
attributes of tours are segue diversity, song arrangement and song familiarity.
More generally, we provide insights into how to present music to users, which
could inform the design of user-centered recommender systems.
