This is the pytorch implementation of GraphCM proposed in the paper: A Graph-Enhanced Click Model for Web Search. SIGIR 2021.
NOTE: The versions of torch-cluster, torch-scatter, torch-sparse, torch-spline-conv are strictly required for torch-geometric package. You can follow the installation instruction in the PyG official website: torch-geometric 1.6.3.
- python 3.7
- pytorch 1.6.0+cu101
- torchvision 0.7.0+cu101
- torch-cluster 1.5.8
- torch-scatter 2.0.5
- torch-sparse 0.6.8
- torch-spline-conv 1.2.0
- torch-geometric 1.6.3
- tensorboardx 2.1
After data pre-processing, we can put all the generated files into ./data/dataset/ folder as input files for GraphCM. Demo input files are available under the ./data/demo/ directory.
The format of train & valid & test & label input files is as follows:
- Each line:
<session id><tab><query id><tab>[<document ids>]<tab>[<vtype ids>]<tab>[<clicks infos>]<tab>[<relevance>]
We provide quick start command in ./run.sh. Note that input files that are related to graph modules are not provided in this repo. You can genenrate graph-related input files using data preprocess files in the ./data_preprocess/ fold.
If you find the resources in this repo useful, please cite our work.
@inproceedings{lin2021graph,
title={A Graph-Enhanced Click Model for Web Search},
author={Lin, Jianghao and Liu, Weiwen and Dai, Xinyi and Zhang, Weinan and Li, Shuai and Tang, Ruiming and He, Xiuqiang and Hao, Jianye and Yu, Yong},
booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={1259--1268},
year={2021}
}