rater 0.1.1

rater

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

1. ml-1m: http://files.grouplens.org/datasets/movielens/ml-1m.zip
2. delicious-2k: http://files.grouplens.org/datasets/hetrec2011/hetrec2011-delicious-2k.zip
3. lastfm-dataset-360K: http://mtg.upf.edu/static/datasets/last.fm/lastfm-dataset-360K.tar.gz
4. slashdot: http://snap.stanford.edu/data/soc-Slashdot0902.txt.gz
5. epinions: http://snap.stanford.edu/data/soc-Epinions1.txt.gz
6. ml-100k: http://files.grouplens.org/datasets/movielens/ml-100k.zip
7. Criteo(dac full): https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz
8. 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

Apache License 2.0

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