ExIM: Exploring Intent of Music Listening for Retrieving User-generated Playlists

Music psychology has identified various reasons why people listen to music, based on empirical evidence from interviews and surveys. In this paper, we take a data-driven approach that adopts both pre-trained Sentence Transformers and Cross Encoder, as well as graph-based clustering to first determine music listening intents and then explore user-generated playlists by comparing the title to the listening intents. For this purpose, we first investigated whether 129 established listening functions, previously identified by Schäfer et al. (2013), could be meaningfully clustered into broader listening intents. While Schäfer et al. (2013) introduced three broad dimensions of music listening, this work aimed to identify smaller, context-specific intents to capture more nuanced intents. The resulting clusters were then evaluated through a first survey to select the clusters of the best performing model. In a second survey, music listening intent clusters were explored in more detail to obtain a deeper understanding of their significance for music retrieval and recommendation. Lastly, the playlist selection per intent and characteristics of different listening intents were further explored through a third survey. Given the encouraging results of the evaluation of the computed clusters (92% of clusters judged consistent by participants) and the insight that more than half of the participants search for playlists for a specific intent, we propose a browsing system that categorizes playlists based on their intent and enables users to explore similar playlists. Our approach is further visualized in a dashboard to explore and browse through playlists in intent space.