Spotify API: Fetching Artist Details like Popularity, Followers, Top Tracks, and its Details

In a fast digital music situation, personalized recommendations play a --key role in improving user experience and satisfaction. This paper examines the development of a personalized music discovery system. This is used by the Spotify -Web -API to interact with user preferences and provide tailor-made content. The system has features such as searching for artists, related artists, related artists covered, persecution of choices based on user input, and pagination of music. The system can invoke detailed information about artists, tracks, and albums, allowing users to explore top tracks based on their preferences, such as tracks, market regions, and more. This paper describes the design, architecture, and implementation of the system, followed by evaluations of its performance and user friendlyness.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-10
DOI
https://doi.org/10.5281/zenodo.14998644
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Spotify API: Fetching Artist Details like Popularity, Followers, Top Tracks, and its Details

Zenodo (CERN European Organization for Nuclear Research)
Computational Physics and Python Applications
article

Spotify API: Fetching Artist Details like Popularity, Followers, Top Tracks, and its Details

article en

Abstract

In a fast digital music situation, personalized recommendations play a --key role in improving user experience and satisfaction. This paper examines the development of a personalized music discovery system. This is used by the Spotify -Web -API to interact with user preferences and provide tailor-made content. The system has features such as searching for artists, related artists, related artists covered, persecution of choices based on user input, and pagination of music. The system can invoke detailed information about artists, tracks, and albums, allowing users to explore top tracks based on their preferences, such as tracks, market regions, and more. This paper describes the design, architecture, and implementation of the system, followed by evaluations of its performance and user friendlyness.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 75%
Computational Physics and Python Applications
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