loading . . . How Growing Up Changes the Way We Hear, and Feel, Music **Summary:** Our music preferences evolve across life โ from youthful exploration to nostalgic reflection. A large-scale analysis of 40,000 usersโ streaming data over 15 years revealed that young listeners engage broadly with new and popular music, while adults settle into more personal and emotionally rooted tastes.
With age, nostalgia becomes a dominant force, shaping listening habits around the music of oneโs youth. These findings highlight how deeply intertwined music, identity, and memory are โ and how platforms can better cater to listeners across generations.
**Key Facts:**
* **Expansive Youth Listening:** Adolescents explore diverse and trendy genres across the musical spectrum.
* **Nostalgic Shift:** Older listeners increasingly return to songs from their youth, reflecting emotional ties and memory.
* **Personalization Insight:** Findings could improve age-tailored music recommendations for streaming services.
**Source:** University of Gothenburg
**Music is a strong marker of identity โ but what we listen to changes with age.**
The results may not be that surprising, but now there is scientific evidence for the first time through an analysis of how listening habits change over time.
The international study from University of Gothenburg, Jรถnkรถping University and University of Primorska, shows that younger users listen to a wide range of contemporary popular music and follow trends in popular culture.
Credit: Neuroscience News
In the transition from adolescence to adulthood, music habits broaden โ more artists and genres are explored, and listening becomes increasingly varied. With age, this spectrum narrows, while music choices become more personal and influenced by previous experiences.
**40,000 users**
โWhen youโre young, you want to experience everything. You donโt go to a music festival just to listen to one particular band, but when you become an adult, youโve usually found a style of music that you identify with. The charts become less important,โ says the studyโs co-author Alan Said, associate professor of computer science at the University of Gothenburg.
The researchers used data from the music service Last.fm, where users share music listening habits from platforms such as Spotify. This makes it possible to build a personal music profile and gain overview of oneโs own music listening.
Since Last.fm users can enter their age when they register, it was possible to link listening habits to age. The study is based on data spanning 15 years and covering more than 40,000 users. The data contained over 542 million plays of more than 1 million different songs.
โIn the study, we can follow how music listening changes over a longer period of time. When companies like Spotify try to develop music recommendations for their customers, they donโt necessarily look at listening habits throughout usersโ lives,โ says Alan Said.
**Nostalgia is a strong driving force**
It turns out that music listening continues to change throughout life. In middle age and beyond, nostalgia becomes a strong driving force; the music from oneโs youth accompanies one as a โsoundtrack of our lives.โ Among older listeners, the patterns is twofold; they continue to engage with new music, but at the same time repeatedly return to songs from their youth.
Musical taste also becomes more unique the older the listener is. Teenagers can find many favourite songs in common with their peers. This becomes more difficult with age. Your neighbour listens to death metal all the time, while you are obsessed with Genesis or reggae.
โMost 65-year-olds donโt embark on a musical exploration journey,โ says Alan Said.
**Improved recommendations**
For companies or individuals behind a recommendation system, such as Spotifyโs suggestions for new music to its users, the studyโs findings present important challenges and opportunities.
This type of lifelong analysis of listening habits hasnโt been possible until recently, simply through the fact they havenโt been around for long enough until now.
โA service that recommends the same type of music in the same way to everyone risks missing what different groups actually want. Younger listeners may benefit from recommendations that mix the latest hits with suggestions for older music they have not yet discovered.
โMiddle-aged listeners appreciate a balance between new and familiar, while older listeners want more tailored recommendations that reflect their personal tastes and nostalgic reminiscences,โ says Alan Said.
### Key Questions Answered:
****Q:** How do music preferences change as people age?**
**A:** Young listeners explore diverse and trendy genres, while adults gradually narrow their focus, finding comfort in familiar favorites and nostalgia.
****Q:** What drives these changes in music taste over time?**
**A:** Maturity brings stronger personal identity, emotional associations, and a tendency to revisit music from formative years rather than follow current trends.
****Q:** Why does this matter for streaming platforms?**
**A:** Understanding age-based listening patterns could help platforms personalize recommendations โ blending discovery for youth, balance for adults, and nostalgia for older listeners.
## About this psychology, music, and aging research news
**Author:**Olof Lรถnnehed
**Source:**University of Gothenburg
**Contact:** Olof Lรถnnehed โ University of Gothenburg
**Image:** The image is credited to Neuroscience News
**Original Research:** Closed access.
โSoundtracks of Our Lives: How Age Influences Musical Preferencesโ by Alan Said et al. _Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
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**Abstract**
**Soundtracks of Our Lives: How Age Influences Musical Preferences**
The majority of research in recommender systems, be it algorithmic improvements, context-awareness, explainability, or other areas, evaluates these systems on datasets that capture user interaction over a relatively limited time span.
However, recommender systems can very well be used continuously for extended time.
Similarly so, user behavior may evolve over that extended time. Although media studies and psychology offer a wealth of research on the evolution of user preferences and behavior as individuals age, there has been scant research in this regard within the realm of user modeling and recommender systems.
In this study, we investigate the evolution of user preferences and behavior using the LFM-2b dataset, which, to our knowledge, is the only dataset that encompasses a sufficiently extensive time frame to permit real longitudinal studies and includes age information about its users.
We identify specific usage and taste preferences directly related to the age of the user, i.e., while younger users tend to listen broadly to contemporary popular music, older users have more elaborate and personalized listening habits.
The findings yield important insights that open new directions for research in recommender systems, providing guidance for future efforts.
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