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