Method | AUC ↑ | MRR ↑ | nDCG@5 ↑ | nDCG@10 ↑ |
---|---|---|---|---|
Random (small) | 0.4998 | 0.3156 | 0.3489 | 0.4338 |
NRMS (small) | 0.5299 | 0.3243 | 0.3625 | 0.4420 |
GERL (small) | 0.5820 | 0.3587 | 0.4032 | 0.4775 |
Method | AUC ↑ | MRR ↑ | nDCG@5 ↑ | nDCG@10 ↑ |
---|---|---|---|---|
GERL (demo) | 0.5344 | 0.3231 | 0.3610 | 0.4436 |
GERL (demo) + weighted sampling | 0.5258 | 0.3172 | 0.3544 | 0.4377 |
Create a virtual environment and install the needed dependencies:
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.lock
Or alternatively, use Rye:
rye sync
. .venv/bin/activate
From the root of the repository, run the preprocessing script:
python -m src.recsys_challenge.dataset.preprocess.auto
To train the model, run:
python -m src.recsys_challenge.training
The default options for training are set to the params we used for training. Options for the training script can be printed using:
python -m src.recsys_challenge.training --help