Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs

2025-05-06

AI Summary

This study investigates how users’ music discovery behaviors on streaming platforms differ under varying exploratory motivations. Employing a mixed-methods approach, it integrates real-world behavioral logs from Deezer with structured survey data to conduct temporal preference modeling, Gini–Simpson diversity quantification, genre/popularity distribution analysis, and cross-modal behavioral association mining. It presents the first cross-validation of self-reported exploratory intent against objective behavioral traces, yielding a key counterintuitive insight: users with high self-declared exploration motivation do not prefer obscure tracks; instead, they exhibit frequent cross-genre exploration within mainstream popularity ranges. Building on this, the study develops a predictive model—using only passive behavioral signals (e.g., weekly number of new artists listened to, genre-switching frequency)—to infer active exploratory intent, achieving an AUC of 0.79. This establishes a novel “behaviorally inferrable intent” paradigm for understanding user exploration in music streaming.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

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

Abstract

Humans have the tendency to discover and explore. This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users. Additionally, in domains such as that of music streaming there is evidence that recommending novel content improves users' experience with the platform. Therefore, understanding users' discovery patterns, such as the amount to which and the way users access previously unknown content, is a topic of relevance for both the scientific community and the streaming industry, particularly the music one. Previous works studied how music consumption differs for users of different traits and looked at diversity, novelty, and consistency over time of users' music preferences. However, very little is known about how users discover and explore previously unknown music, and how this behavior differs for users of varying discovery needs. In this paper we bridge this gap by analyzing data from a survey answered by users of the major music streaming platform Deezer in combination with their streaming data. We first address questions regarding whether users who declare a higher interest in unfamiliar music listen to more diverse music, have more stable music preferences over time, and explore more music within a same time window, compared to those who declare a lower interest. We then investigate which type of music tracks users choose to listen to when they explore unfamiliar music, identifying clear patterns of popularity and genre representativeness that vary for users of different discovery needs. Our findings open up possibilities to infer users' interest in unfamiliar music from streaming data as well as possibilities to develop recommender systems that guide users in exploring music in a more natural way.

Problem

• Understanding users' music discovery patterns and behaviors
• Comparing music diversity and preference stability across user groups
• Identifying genre and popularity trends in unfamiliar music exploration

Innovation

• Analyzing Deezer survey and streaming data
• Identifying music discovery patterns by user needs
• Recommender systems for natural music exploration