ZhuStory-ll / GATE

The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"

The GATE model for Content-aware Recommendation

The implementation of the paper:

Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, and Xue Liu, "Gated Attentive-Autoencoder for Content-Aware Recommendation", in the 12th ACM International Conference on Web Search and Data Mining (WSDM 2019)

Arxiv: https://arxiv.org/abs/1812.02869

Please cite our paper if you use our code. Thanks!

Author: Chen Ma ([e-mail])

Bibtex

@inproceedings{DBLP:conf/wsdm/MaKWWL19,
  author    = {Chen Ma and
               Peng Kang and
               Bin Wu and
               Qinglong Wang and
               Xue Liu},
  title     = {Gated Attentive-Autoencoder for Content-Aware Recommendation},
  booktitle = {{WSDM}},
  pages     = {519--527},
  publisher = {{ACM}},
  year      = {2019}
}

Environments

• python 3.6
• PyTorch (version: 0.4.0)
• numpy (version: 1.15.0)
• scipy (version: 1.1.0)
• sklearn (version: 0.19.1)

Dataset

In our experiments, the citeulike-a dataset is from http://www.wanghao.in/CDL.htm, the movielens-20M dataset is from https://grouplens.org/datasets/movielens/20m/, the Amazon-CDs and Amazon-Books datasets are from http://jmcauley.ucsd.edu/data/amazon/.

Example to run the code

Data preprocessing:

The code for data preprocessing is put in the /preprocessing folder. Amazon_CDs.ipynb provides an example on how to transform the raw data into the .pickle files that used in our program.

Train and evaluate the model (you are strongly recommended to run the program on a machine with GPU):

python run.py