ilya-shenbin / RecVAE

The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"

RecVAE

The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback".

In order to train RecVAE on MovieLens-20M dataset (download link), preprocess it using following script:

python preprocessing.py --dataset <path_to_csv_file> --output_dir <dataset_dir> --threshold 3.5 --heldout_users 10000

You can also use another dataset, it should contain columns userId, movieId and rating (in arbitrary order).

Use the following command to reproduce results of RecVAE on MovieLens-20M:

python run.py --dataset <dataset_dir> --n-epochs 50 --gamma 0.005

Use the following command to reproduce results of RecVAE+ImplicitSLIM (github, paper) on MovieLens-20M:

python run.py --dataset <dataset_dir> --n-epochs 100 --latent-dim 400 --gamma 0.004 --implicitslim --step 10 --lambd 500 --alpha 1 --threshold 1000

See python run.py -h for more information.

Current model is slightly different from the one described in the paper, you can find original code here.

Some sources from Variational autoencoders for collaborative filtering is partially used.

If you used this code for a publication, please cite our WSDM'20 paper

@inproceedings{10.1145/3336191.3371831,
  author = {Shenbin, Ilya and Alekseev, Anton and Tutubalina, Elena and Malykh, Valentin and Nikolenko, Sergey I.},
  title = {RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback},
  year = {2020},
  isbn = {9781450368223},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3336191.3371831},
  doi = {10.1145/3336191.3371831},
  booktitle = {Proceedings of the 13th International Conference on Web Search and Data Mining},
  pages = {528–536},
  numpages = {9},
  keywords = {deep learning, collaborative filtering, variational autoencoders},
  location = {Houston, TX, USA},
  series = {WSDM ’20}
}