Modeling Activity-Driven Music Listening with PACE

While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted.

In this paper, we propose PACE (PAttern-based user Consumption Embedding), a framework for building user embeddings that takes advantage of periodic listening behaviors.

PACE leverages users’ multichannel time-series consumption patterns to build understandable user vectors. We believe the embeddings learned with PACE unveil much about the repetitive nature of user listening dynamics.

By applying this framework on long-term user histories, we evaluate the embeddings through a predictive task of activities performed while listening to music.

The validation task’s interest is two-fold, while it shows the relevance of our approach, it also offers an insightful way of understanding users’ musical consumption habits.

This paper has been published in the proceedings of the 2024 Conference on Human Information Interaction and Retrieval (CHIIR ‘24).