# Modeling Musical Genre Trajectories through Pathlet Learning

arXiv: https://arxiv.org/abs/2505.03480 · HTML: https://mlsanity.com/paper/2505.03480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 13 of 13)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Current Challenges and Visions in Music Recommender Systems Research](https://mlsanity.com/paper/1710.03208/) (2017)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Explainable Trajectory Representation through Dictionary Learning](https://mlsanity.com/paper/2312.08052/) (2023)
- [A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation](https://mlsanity.com/paper/2212.07030/) (2022)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [Follow the guides: disentangling human and algorithmic curation in online music consumption](https://mlsanity.com/paper/2109.03915/) (2021)
- [Modeling Activity-Driven Music Listening with PACE](https://mlsanity.com/paper/2405.01417/) (2024)
- [Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems](https://mlsanity.com/paper/2405.02026/) (2024)
- [Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data](https://mlsanity.com/paper/2408.16430/) (2024)
- [Uplifting Interviews in Social Science with Individual Data Visualization: the case of Music Listening](https://mlsanity.com/paper/2204.06809/) (2022)

## Abstract (arXiv)

The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user preferences remains a complex challenge, particularly as their musical tastes change over time. This paper uses the dictionary learning paradigm to model user trajectories across different musical genres. We define a new framework that captures recurring patterns in genre trajectories, called pathlets, enabling the creation of comprehensible trajectory embeddings. We show that pathlet learning reveals relevant listening patterns that can be analyzed both qualitatively and quantitatively. This work improves our understanding of users' interactions with music and opens up avenues of research into user behavior and fostering diversity in recommender systems. A dataset of 2000 user histories tagged by genre over 17 months, supplied by Deezer (a leading music streaming company), is also released with the code.