State of the art model for MovieLens-1M.
This is a minimal implementation of a kernelNet sparsified autoencoder for MovieLens-1M. See http://proceedings.mlr.press/v80/muller18a.html
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- numpy
- scipy
- tensorflow (tested with version 1.13)
Expects MovieLens-1M dataset in a subdirectory named ml-1m. Get it here https://grouplens.org/datasets/movielens/1m/
or on linux run in the project directory
wget --output-document=ml-1m.zip http://www.grouplens.org/system/files/ml-1m.zip; unzip ml-1m.zip
python kernelNet_ml1m.py
optional arguments are the L2 and sparsity regularization strength. Default is 60. and 0.013
with the default parameters this slightly outperforms the paper model at 0.823 validation RMSE (10-times repeated random sub-sampling validation)