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Project description


rater is a comparative framework for multimodal recommender systems. It was developed to facilitate the designing, comparing, and sharing of recommendation models.



  1. ml-1m:
  2. delicious-2k:
  3. lastfm-dataset-360K:
  4. slashdot:
  5. epinions:
  6. ml-100k:
  7. Criteo(dac full):
  8. Criteo(dac sample):


pip3 install rater


git clone
cd rater
python3 install


Load the built-in MovieLens 1M dataset (will be downloaded if not cached):


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.


The models supported are listed below. Why don't you join us to lengthen the list?


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.


Apache License 2.0


  • [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

Project details

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