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.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Coache, Samuel Christopher
• Averill, Quinn S
• Baker, Jackson Craig
• McAteer, Steven

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-032219-132355

Advisor

• Kong, Xiangnan
• Lee, Kyumin

Year

• 2019

Date created

• 2019-03-22

Resource type

• Major Qualifying Project

Major

• Computer Science

Rights statement

• In Copyright