Modeling Musical Genre Trajectories through Pathlet Learning

2025-05-06

AI Summary

This paper investigates the dynamic evolution of users’ music preferences over time. To model such temporal patterns, we propose *pathlets*—interpretable and reusable units representing characteristic listening trajectory motifs. We introduce dictionary learning to music trajectory modeling for the first time, automatically discovering cross-user common evolutionary pathways from genre sequences. By jointly optimizing a pathlet dictionary and trajectory embeddings, our approach yields compact, semantically meaningful, and generalizable temporal preference representations. Evaluated on a real-world dataset from Deezer comprising 2,000 users and 17 months of genre-level behavioral data, the learned pathlets effectively uncover canonical listening evolution patterns—including genre expansion, reversion, and migration—substantially enhancing trajectory interpretability and improving downstream diversity-aware recommendation performance. Our framework establishes a novel paradigm for dynamic music preference modeling and personalized service delivery.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Modeling user genre preferences evolution over time
• Identifying recurring patterns in musical genre trajectories
• Improving music recommender systems through trajectory analysis

Innovation

• Uses dictionary learning for genre trajectories
• Introduces pathlets to capture recurring patterns
• Analyzes listening patterns qualitatively and quantitatively