@Author: Zeyu Li, Jyun-Yu Jiang, Yizhou Sun, Wei Wang
This is the repo of "Personalized Question Routing via Heterogeneous Network Embedding". Bibtex citation format:
@inproceedings{li2019personalized,
title={Personlaized Question Routing via Heterogeneous Network Embedding},
author={Li, Zeyu and Jiang, Jyun-Yu and Sun, Yizhou and Wang, Wei},
year={2019},
organization={AAAI Conference on Artificial Intelligence (AAAI)}
}
NeRank is prototyped in PyTorch 0.4.0a0. Other package dependencies are listed in "requirements.txt". You can run
$ pip install -r requirements.txt
to install the packages that are needed.
The archive of the dataset is available here. Download the dataset and unzip the 7z files into ./raw/. Then run
$ python src/preprocessing.py [name of dataset] [threshold] [prop of test] [test sample size]
to preprocess the XML to json.
Parameters:
name of dataset: name of the dataset, such as "Biology", "English", and "3dpringting".threshold: #. of entities to be selected as for the training.prop of test: float. proportion of questions for testing.test sample size: number of candidate questions in each test samples. The produced files will appear in./data/.
Generate the input pairs by generate_walk.py using the following command.
$ python src/generate_walk.py [name of dataset] [length] [num_walk] [window_size]
Parameters:
name of dataset: Similar to above.length: the length of the generated walks.num_walk: the number of times each node is covered by walks.window_size: the size of Skip-gram sampling window.
Run NeRank by the following command:
$ ./run.sh
But before running run.sh, please look into it and tweak the settings.
Parameter:
ID: the identifier of a certain training/testing, will be used in output file name. You would see the performance in./performance/.