Simple but flexible Deep Recommenders in PyTorch.
Plese review the deck to see the accompanying written & visual content.
Check out the notebooks within to step through variations of matrix factorization models. Here’s what we’ll cover:
If at all possible, please check out and pre-install the environment.
git clone https://github.com/cemoody/simple_mf.git cd simple_mf
Create the environment by following the steps below. If you choose to use your own environment, you’ll need access to have the Python packages in
Make sure you you have pytorch installed; if not, follow the instructions here
pip install pytorch-lightning optuna
Follow the directions the above command spits out.
You’ll be creating using a (free) weights & biases account to track model metrics and performance over time. TO kickstart that process:
pip install wandb wandb login
Setup your W&B account, then go to the W&B authorization page: https://app.wandb.ai/authorize and copy the auth code into your terminal when prompted by
This will download and preprocess the MovieLens 1M dataset. We’ll use this canonical dataset to test drive our code.
# required. will download the movielens 1M dataset. python src/download.py # optional! this is a bigger dataset we'll use for more # advanced models. python src/download_ml20.py # optional too. This is used for word2vec notebook. python src/skipgram.py
Open up and execute every line within the
01 MF model.ipynb notebook. If it works, you’re golden.