An Analysis of the Evolution of Music Listening Data and the Need for Task Discernment

Abstract

With the availability of music streaming platforms, listening behavior has seen fundamental changes in the past two decades, going from mere consumption of and recommendation within personal collections to an exploration of massive catalogs. As part of this trend, collaborative filtering algorithms that exploit consumption data, user feedback, and, most recently, the sequential order of music consumption, have become indispensable. In prior work, it has been shown that the incorporation of negative feedback (skipped track information) via contrastive learning can be applied to and improve existing sequential recommendation models. In this work, we extend previous findings by investigating two notable aspects of music listening data in detail. First, we analyze popular public datasets used in music recommender systems research (LFM-1k, LFM-2B, and the Music Streaming Sessions Dataset) with respect to the evolution of consumption activity and track skipping behavior, and show strongly deviating patterns based on data creation context. Second, focusing on LFM-2B, we further study the impact of data and skipping information availability on sequential and non-sequential recommendation algorithms over the different years available in the data set. We observe deviating model performance using time-based subsets of LFM-2B compared to experiments on the entire dataset. In conclusion, we argue for more careful discernment and understanding of listening tasks and user intents leading to creating datasets, as well as explicitly modeling different types of interactions.