A Graph-Enhanced Click Model for Web Search (GraphCM)

Introduction

This is the pytorch implementation of GraphCM proposed in the paper: A Graph-Enhanced Click Model for Web Search. SIGIR 2021.

Requirements

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

Input Data Formats

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>]

Quick Start

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.

Citation

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}
}