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Certificate of Completion
THIS ACKNOWLEDGES THAT
HAS COMPLETED THE FALL 2023 DATA SCIENCE BOOT CAMP
Kashish Mehta
Roman Holowinsky, PhD
DECEMBER 07, 2023
DIRECTOR
DATE
TEAM
Funk
aydin ozbek, Dane Miyata, Kristina Knowles, Mario Gomez, Kashish Mehta
Most existing music recommendation systems rely on listeners to provide seed tracks, and then utilize a variety of different approaches to recommend additional tracks in either a playlist-like listening session or as sequential track recommendations based on user feedback.
We built a playlist recommendation engine that takes a different approach, allowing listeners to generate a novel playlist based on a semantic string, such as the title of desired playlist, specific mood (happy, relaxed), atmosphere (tropical vibe), or function (party music, focus). Using a publicly available dataset of existing playlists (https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge), we combine a semantic similarity vector model with a matrix factorization model to allow users to quickly and easily generate playlists to fit any occasion.