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The Impact of Spotify’s AI-Driven Music Recommender on User Listener Habits

Thingstad, Jørgen
Master thesis
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no.uia:inspera:143809141:44758143.pdf (5.319Mb)
URI
https://hdl.handle.net/11250/3082199
Date
2023
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  • Master's theses in Music Performance [219]
Abstract
This study explores how Spotify uses AI-technology to collect data about the user’s music listening behavior and serve personalized music recommendations based on their music taste and listening habits. It also involves a quantitative survey to discover the impact these AI- driven algorithms have on the Spotify users, especially focusing on four carefully chosen aspects: the user’s satisfaction with the music recommendations, the correlation between their satisfaction and their user activity, their selectivity in song choices and their ways of discovering new music. The results from the survey indicates that there is an overall satisfaction with the music personalization, especially for the most active users. Also, their reports indicate that they prefer the mix between familiarity and music discovery, and that they don’t believe the recommendations have a significant impact on their selectivity.
 
 
 
Publisher
University of Agder

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