MELU-Pytorch

Unofficial PyTorch implementation of MELU from the paper: MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation.

Platform

• python: 3.5+
• Pytorch: 1.0+

Model

We offer the training process and model in multi_result_files/9b8290dd3f63cbafcd141ba21282c783.pkl.

How to run

Training

python3 maml.py

You can modify the detailed parameters according to the definition in maml.py.

Testing

python3 maml.py --test

By default, you can directly add the test argument to test the model obtained from the same aruguments setting.

mode_path = utils.get_path_from_args(args)

You can also modify the code in maml.py manually since the arguments may vary for training and testing process.

mode_path = '9b8290dd3f63cbafcd141ba21282c783'

Benchmark

The official code doesn't offer evaluation code for testing. So based on this implementation, you can test the MAE for 4 partitions. In addition, we find the hyperparameters setting in original paepr isn't reasonable so we modify that and rerun the test.

Partition	MAE based on our hyperparameters	Reported MAE in original paper
Existing items for existing users	0.68$\pm$0.01	0.75$\pm $NA
Existing items for new users	0.74$\pm$0.01	0.79$\pm$NA
new items for existing users	0.90$\pm$0.01	0.92$\pm$NA
new items for new users	0.89$\pm$0.02	0.92$\pm$NA

Acknowledgement.

This code refers code from: wyharveychen/CloserLookFewShot. lmzintgraf/cavia. hoyeoplee/MeLU.