This directory contains two notebooks: music_recommender_EDA - contains all EDA that was performed on the music recommendation data set, as well as all tranformations/corrections applied to the data music_ratings_learner - contains the probabilistic matrix factorization model implementation, as well as all code for data loaders, training the model, etc. I wrote an entire article explaining the theory and implementation in this project that can be found at https://towardsdatascience.com/building-a-music-recommendation-engine-with-probabilistic-matrix-factorization-in-pytorch-7d2934067d4a
wolfecameron/music_recommendation
contains the notebooks that I created for implementing probabilistic matrix factorization in PyTorch for music recommendation engine.
Jupyter Notebook