rater
Project description
rater
rater is a comparative framework for multimodal recommender systems. It was developed to facilitate the designing, comparing, and sharing of recommendation models.
Feature
Data
- ml-1m: http://files.grouplens.org/datasets/movielens/ml-1m.zip
- delicious-2k: http://files.grouplens.org/datasets/hetrec2011/hetrec2011-delicious-2k.zip
- lastfm-dataset-360K: http://mtg.upf.edu/static/datasets/last.fm/lastfm-dataset-360K.tar.gz
- slashdot: http://snap.stanford.edu/data/soc-Slashdot0902.txt.gz
- epinions: http://snap.stanford.edu/data/soc-Epinions1.txt.gz
- ml-100k: http://files.grouplens.org/datasets/movielens/ml-100k.zip
- Criteo(dac full): https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz
- Criteo(dac sample): http://labs.criteo.com/wp-content/uploads/2015/04/dac_sample.tar.gz
Install
pip3 install rater
or
git clone https://github.com/shibing624/rater.git
cd rater
python3 setup.py install
Usage
Load the built-in MovieLens 1M dataset (will be downloaded if not cached):
Output:
MAE | RMSE | AUC | NDCG@10 | NDCG@20 | Recall@10 | Recall@20 | Train (s) | Test (s) | |
---|---|---|---|---|---|---|---|---|---|
MF | 0.7430 | 0.8998 | 0.7445 | 0.0479 | 0.0556 | 0.0352 | 0.0654 | 0.13 | 1.57 |
PMF | 0.7534 | 0.9138 | 0.7744 | 0.0617 | 0.0719 | 0.0479 | 0.0880 | 2.18 | 1.64 |
BPR | N/A | N/A | 0.8695 | 0.0975 | 0.1129 | 0.0891 | 0.1449 | 3.74 | 1.49 |
For more details, please take a look at our examples.
Models
The models supported are listed below. Why don't you join us to lengthen the list?
Contribute
Your contributions at any level of the library are welcome. If you intend to contribute, please:
- Fork the rater repository to your own account.
- Make changes and create pull requests.
You can also post bug reports and feature requests in GitHub issues.
License
Reference
- [Multilayer Perceptron Based Recommendation]
- [Autoencoder Based Recommendation]
- [CNN Based Recommendation]
- [RNN Based Recommendation]
- [Restricted Boltzmann Machine Based Recommendation]
- [Neural Attention Based Recommendation]
- [Neural AutoRegressive Based Recommendation]
- [Deep Reinforcement Learning for Recommendation]
- [GAN Based Recommendation]
- [Deep Hybrid Models for Recommendation]
- maciejkula/spotlight
- shenweichen/DeepCTR
- [推荐系统实践]
- Magic-Bubble/RecommendSystemPractice
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