Exploring playlist titles for cold-start music recommendation: an effectiveness analysis

Abstract

In music recommender systems, automatic playlist continuation is an emerging task that aims to improve users’ listening experience by recommending music in line with their musical taste. The typical approach towards this goal is to identify playlist characteristics by inspecting the existing tracks (i.e., seeds) in target playlists. However, seeds are not always available, especially when users create new playlists. For such cold-start situations, user-generated titles can be a good starting point to understand the intended purpose of users. This paper investigates the effectiveness of titles as an auxiliary data source for playlists suffering from the cold-start problem. Employing three naive recommendation models, we conduct experiments on one million music playlists from the Spotify platform. Our analyses show that the prevalent attitude in naming playlists results in highly accurate recommendations for playlists concerning a specific theme, such as albums, artists, and soundtracks. As the title space moves away from a particular theme, recommendation accuracy drops. Furthermore, the correlation between the common preference of a title and its usability in recommendation is quite weak; a title without a common sense may be useless in recommender systems, even though many users favor that title. Consequently, our findings serve as a guideline to develop title-aware recommendation approaches that can provide coherent continuations to the cold-start playlists.