Song Recommendation Engine Using Spotify Web API

Paige Yang, Winona State University

Abstract

This poster highlights a study on using Spotify's Web API to create personalized song recommendations based on users' existing playlists. Spotify's user-friendly platform allows users to create custom playlists, discover new music, and share tracks with others. By leveraging the Web API, this study demonstrates how custom recommendations can enhance the music experience by helping users discover new tracks and rediscover old favorites. These findings showcase the potential of Spotify to revolutionize the way people consume and interact with music.

College

College of Science & Engineering

Department

Mathematics & Statistics

Campus

Winona

First Advisor/Mentor

April Kerby-Helm

Start Date

4-19-2023 9:00 AM

End Date

4-19-2023 10:00 AM

Presentation Type

Poster Session

Format of Presentation or Performance

In-Person

Session

1a=9am-10am

Poster Number

44

Share

COinS
 
Apr 19th, 9:00 AM Apr 19th, 10:00 AM

Song Recommendation Engine Using Spotify Web API

This poster highlights a study on using Spotify's Web API to create personalized song recommendations based on users' existing playlists. Spotify's user-friendly platform allows users to create custom playlists, discover new music, and share tracks with others. By leveraging the Web API, this study demonstrates how custom recommendations can enhance the music experience by helping users discover new tracks and rediscover old favorites. These findings showcase the potential of Spotify to revolutionize the way people consume and interact with music.