Faculty Advisor

Kong, Xiangnan

Faculty Advisor

Lee, Kyumin

Abstract

The goal of this project is to develop a recommender system that derives song recommendations from an implicit music dataset provided by the streaming service Spotify. We implemented current baseline systems and then two advancements over the baselines: Feature Enhanced Matrix Factorization and Non-Linear Matrix Factorization. To compare these systems, we took the predicted songs for a given playlist and calculated the performance score based on the accuracy of those results. We then compared the results from these NDCG scores to determine which system performed the best for the given Spotify dataset. Based off of the results, we were able to draw conclusions regarding the design process for an effective recommender system for music data.

Publisher

Worcester Polytechnic Institute

Date Accepted

March 2019

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

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