Implementation of "CPMF: A Collective Pairwise Matrix Factorization Model for Upcoming Event Recommendation".

If you find this method helpful for your research, please cite this paper:

@inproceedings{LiuZWXHG17,
  author    = {Chun{-}Yi Liu and
               Chuan Zhou and
               Jia Wu and
               Hongtao Xie and
               Yue Hu and
               Li Guo},
  title     = {{CPMF:} {A} collective pairwise matrix factorization model for upcoming event recommendation},
  booktitle = {2017 International Joint Conference on Neural Networks, {IJCNN} 2017},
  pages     = {1532--1539},
  year      = {2017}
}

• python >= 3.4
• numpy
• tqdm
• sklearn

The dataset can be downloaded from large network. Or connect the author of the paper "Event-based Social Networks: Linking the Online and Offline Social Worlds".

We extract the dataset of a city from the original dataset as follows:

python main.py --mode=prepro --pre_config=xxxx

The pre_config need to be specified. It is a .json file.

The process will generate several file in the saved path, including user.loc, event.loc, ue.pair, ug.pair, eg.pair and info.json.

Then, we can train our model with:

python main.py --mode=run --city_info_path=xxxxx --train_path=yyy --dev_path=zzz

The city_info_path is the path of the generated info.json. The input total datasets will first divide into a training dataset and a evaluation dataset. The saved paths can be specified by train_path and dev_path respectively. The saved files are both .json files. The ratio of the evaluation dataset is specified by test_ratio.

If there exists both the train_path and dev_path, the pre-divided training and evaluation datasets will be loaded to avoid re-split.

Then, the model will be trained, and we can observe the performance of the training and evaluation dataset. And the model is auto saved in the training process. Note only one model will be saved, and the user should monitor the performance and manually conduct the early stopping.

There are also arguments to set the hyper-parameter of the model.and the introduction can be find in the main.py.

Given the evaluation dataset, we can conduct the evaluation with:

python main.py --mode=eval --dev_path=xxxxx

The dev_path is the path of the evaluation file.

The original codes were lost. I have attempted to re-create them as faithfully as possible but there may be some issues. If you find any bugs, please report them to me.