This project was created during the class Personalization Theory and Application, taught by Brett Vintch at Columbia University, Fall 2017
Collaborators on this project are:
- Jan-Felix Schneider
- Manksh Gupta
- Andres Potapczynski
- Mohamed Maskani Filali
In this final project we explored the use of a biased matrix factorization model to improve the prediction of accuracy ratings for a music recommender system. To train the system we used data from the music service Deezer. As a baseline we were using neighborhood based colloborative filtering.
The final report can be found here.
It contains a detailed description of the data, our methodology and our results.
The different models (baseline, matrix factorization, neighborhood filtering) can be found here