Date of Award
Thesis - SCU Access Only
Santa Clara : Santa Clara University, 2023.
Computer Science and Engineering
Many popular media recommendation algorithms suffer from over recommending the most popular results. We wanted to create a music recommendation system that relied completely on song data and no demographic data to provide less biased song results. We created a Python and Flask web application that takes data from Spotify about songs and targets similar songs using the K-Nearest Neighbors algorithm. Our user interface allows users to login to Spotify, select songs as seeds for our algorithm, and then examine data about the recommended results. Our algorithm provided recommendations that were rated on average half as popular as Spotify’s algorithm, and provided almost twice as much coverage of long-tail items. The algorithm shows promising results, and with further testing, computing power, and time, it could be improved even more.
Seminatore, Matthew and Hay, Peter, "Finder: A Diverse Music Recommendation System" (2023). Computer Science and Engineering Senior Theses. 255.
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