On skipping behaviour types in music streaming sessions

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Abstract

The ability to skip songs is a core feature in modern online streaming services. Its introduction has led to a new music listening paradigm and has changed the way users interact with the underlying services. Thus, understanding their skipping activity during listening sessions has acquired considerable importance. This is because such implicit feedback signal can be considered a measure of users' satisfaction (dissatisfaction or lack of interest), affecting their engagement with the platforms. Prior work has mainly focused on analysing the skipping activity at an individual song level. In this work, we investigate different behaviours during entire listening sessions with regards to the users' session-based skipping activity. To this end, we propose a data transformation and clustering-based approach to identify and categorise skipping types. Experimental results on the real-world music streaming dataset (Spotify) indicate four main types of session skipping behaviour. A subsequent analysis of short, medium, and long listening sessions demonstrate that these session skipping types are consistent across sessions of varying length. Furthermore, we discuss their distributional differences under various listening context information, i.e. day types (i.e. weekday and weekend), times of the day, and playlist types.
Original languageEnglish
Title of host publicationCIKM '21
Subtitle of host publicationProceedings of the 30th ACM International Conference on Information & Knowledge Management
Place of PublicationNew York
Pages3333-3337
Number of pages5
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - 26 Oct 2021

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Keywords

  • Spotify
  • session
  • skipping
  • user behaviour
  • listening
  • music

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