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DeepRec

In this repository, we implement many recent deep learning based recommendation models with Tensorflow.

Implemented Algorithms

We implemented both rating estimation, top-n recommendation models and sequence-aware recommendation models.

  • I-AutoRec and U-AutoRec (www'15)
  • CDAE (WSDM'16)
  • NeuMF (WWW'17)
  • CML (WWW'17)
  • LRML (WWW'18) (DRAFT ONLY, testing will come soon)
  • NFM (SIGIR'17)
  • NNMF (arxiv)
  • PRME (IJCAI 2015)
  • CASER (WSDM 2018)
  • AttRec (AAAI 2019 RecNLP) and so on.

You can run this code from Test/test_item_ranking.py, Test/test_rating_pred.py, or Test/testSeqRec.py

Requirements

  • Tensorflow 1.7+, Python 3.5+, numpy, scipy, sklearn, pandas

To do

  • Add more models
  • Different Evaluation Protocals
  • Code Refactor

Citation

To acknowledge use of this open source package in publications, please cite the following paper:

@article{zhang2019deeprec,
  title={Deep learning based recommender system: A survey and new perspectives},
  author={Zhang, Shuai and Yao, Lina and Sun, Aixin and Tay, Yi},
  journal={ACM Computing Surveys (CSUR)},
  volume={52},
  number={1},
  pages={5},
  year={2019},
  publisher={ACM}
}

Thank you for your support!!!

Contributions and issues are always welcome. You can also contact me via email: cheungshuai@outlook.com

Collaborators

Shuai Zhang, Yi Tay, Bin Wu