The Fast and Winding Roads that Lead to The Doors: Generating Heterogeneous Music Playlists

Consider the problem of creating a wedding reception playlist. Such playlist should ideally satisfy a very diverse crowd by finding a perfect eclectic mix of songs to keep everyone satisfied. In fact, music playlists for large parties are usually composed by a very heterogeneous set of songs, so everyone can listen to songs of their liking at some point. Such playlists, which we call heterogeneous playlists, are also very appropriate in dynamic contexts, when the mood of the listener changes with time, such as workout sessions or road trips. The challenge of automatically generating heterogeneous playlists is to find the appropriate balance among several conflicting goals. For instance, the generated playlist should have smooth transitions between successive tracks while covering a highly diverse set of songs in the time the user has available to her/him. In this paper, we formulate the problem of automatically generating high quality heterogeneous playlists and propose two methods for solving it, namely ROPE and STRAW. We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs. When compared with the state of the art algorithms, ROPE and STRAW are the only ones that can effectively satisfy all of the following quality constraints: heterogeneity, smooth transitions, novelty, scalability and usability.