xrosliang / 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 ([email protected])

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/. (If you need the data after preprocessing, please send me an email).

The XXX_user_records.pkl file is a list of lists that stores the inner item id of each user, e.g., user_records[0]=[item_id0, item_id1, item_id2,...].

The XXX_user_mapping.pkl file is a list that maps the user inner id to its original id, e.g., user_mapping[0]=A2SUAM1J3GNN3B.

The XXX_item_mapping.pkl file is similar to XXX_user_mapping.pkl.

The item_relation.pkl file is a list of lists that stores the neighbors of each item, e.g., item_relation[0]=[item_id0, item_id1, item_id2,...].

The review_word_sequence.pkl file is a list of lists that stores the word sequence in the description of each item , e.g., review_word_sequence[0]=[word_id0, word_id1, word_id2,...]. The word id is the same as the line number (start from 0) in the vocabulary.txt file.

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