README.md

Boltzmann Machines for Collaborative Filtering

This is a series of Boltzmann Machines models for Collaborative Filtering implemented in PyTorch, TensorFlow, and Keras.

Here are the 3 different models:

• Restricted Boltzmann Machines (paper)
• Explainable Restricted Boltzmann Machines (paper)
• Neural Autoregressive Distribution Estimator (paper)

Download and Process Data

You can download the MovieLens-1M dataset from this folder.

Training Models

To run the Restricted Boltzmann Machines model:

python RBM-CF-PyTorch/train.py

To run the Explainable Restricted Boltzmann Machines model:

python Explainable-RBM-CF-TensorFlow/main.py

To run the Neural Autoregressive Distribution Estimator model:

python NADE-CF-Keras/run.py

Evaluating Results

Here are the results for all three models after 50 epochs of training:

Model	RMSE	Runtime
RBM	0.590	10m56s
Explainable RBM	0.3116	1m43s
NADE	0.920	90m45s

• Explainable RBM model has the lowest RMSE and shortest training time.
• NADE, on the other hand, has the highest RMSE and longest training time.