Effects of algorithmic curation in users’ music taste on Spotify
DOI:
https://doi.org/10.23882/rmd.24258Keywords:
Spotify, algorithmic curation, digital platforms, calculated publics, taste, platformization, filter bubblesAbstract
This study addresses the potential impact of recommendation algorithms on Spotify users' musical tastes with the aim of understanding how algorithmic suggestions shape listening behaviors and preferences. A comprehensive review of the literature reveals that the presence of algorithms has contributed to reduced musical diversity and increased taste tautology among users. The results suggest that recommendation algorithms reinforce prior preferences, leading to the emergence of filter bubbles This algorithm-driven taste has obvious cultural implications and, with it, a large impact on the overall diversity of the musical experience. A qualitative methodology was used, consisting of a systematic literature review based on the PRISMA framework, identifying trends and key elements of existing studies. This study finds its limitations in the need for an additional quantitative study to delve deeper into the behavior of recommendation algorithms. Ultimately, this research underscores the need for greater awareness of the implications of music recommendation using algorithms in the digital age.
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