Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs

TL;DR

This study analyzes how music streaming users discover and explore unfamiliar music, revealing patterns related to their discovery needs and informing personalized recommendation strategies.

Contribution

It bridges the gap by combining survey data with streaming behavior to understand discovery patterns and preferences for unfamiliar music.

Findings

Users with higher interest in unfamiliar music listen to more diverse tracks.

Discovery needs influence genre and popularity preferences during exploration.

Patterns identified can improve recommendation systems for natural music exploration.

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.