Analysis of the data located at Kaggle MovieLens 100K and building of a recommender system using PyTorch embeddings to infer the latent factors of both users and movies.

You can follow up this notebook in your machine by downloading Anaconda and then running the following command in the terminal:

conda env create --file movielens-recommender.yml

This should have you prepared to run jupyter notebook in the terminal and then running the mentioned code using that familiar interface.

Please note that I've used a M1 Mac, so the installation might suffer from some problems if you are using a different CPU architecture. In those cases do not hesitate to look up information on the official sources such as PyTorch or PyPi to solve your occasional errors during the installation.

• Deep Learning for Coders with fastai and PyTorch
• Google - Recommendation Systems - Collaborative Filtering
• LSTM for Real Time Recommendation Systems