Musical genres are constructed labels used to describe music that people tend to group together. As music becomes more accessible, consumers aim to expand their musical tastes more and more. Streaming services offer robust song recommendations, but fail to recommend genres. We attempt to implement various similarity and clustering methods to generate meaningful genre recommendations based off of a Spotify user's top tracks. Through our research, we explore methods of parameter optimization and novel preprocessing methods.
Computer Science; Mathematics
Williams, Angelo, "Exploring Spotify Genre Recommendation Systems" (2021). Senior Independent Study Theses. Paper 9290.
Databases and Information Systems | Data Science | Software Engineering
Music, Genres, Recommendation System, Recommender System, Clustering, Database, Similarity, Spotify
Bachelor of Arts
Senior Independent Study Thesis
© Copyright 2021 Angelo Williams