deepRec

Using deep learning models for building recommendation systems.

Dataset

We used the ml-1m dataset for explicit and implicit ratings.

To train an RBM model run

python -m rbm.train

To train a VAE model run

python -m vae.train

Name		Name	Last commit message	Last commit date
Latest commit History 112 Commits
backend		backend
data		data
frontend @ efccc31		frontend @ efccc31
metrics		metrics
rbm		rbm
recs		recs
results		results
utils		utils
vae		vae
.gitignore		.gitignore
.gitmodules		.gitmodules
README.md		README.md
eda.ipynb		eda.ipynb
eval.sh		eval.sh
evaluate.ipynb		evaluate.ipynb
evaluation.py		evaluation.py
example		example
examples.ipynb		examples.ipynb
metrics.feather		metrics.feather
metrics.py		metrics.py
metrics.tex		metrics.tex
metrics2.feather		metrics2.feather
metrics_table.tex		metrics_table.tex
nohup.out		nohup.out
requirements.txt		requirements.txt
res_with_num_ratings.feather		res_with_num_ratings.feather
visuals.ipynb		visuals.ipynb
viz.ipynb		viz.ipynb