The code KDD17 paper "Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks"

Readers are welcomed to fork this repository to reproduce the experiments and follow our work. Please kindly cite our paper

@inproceedings{zhao2017meta,
title={Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks},
author={Zhao, Huan and Yao, Quanming and Li, Jianda and Song, Yangqiu and Lee, Dik Lun},
booktitle={KDD},
pages={635--644},
year={2017}
}

For the sake of ease, a quick instruction is given for readers to reproduce the whole process on yelp-50k dataset. Note that the programs are testd on Linux(CentOS release 6.9), Python 2.7 from Anaconda 4.3.6.

1. Download the yelp-50k and amazon-50k : https://www.dropbox.com/s/io915s8mdwph46e/data.zip?dl=0
2. Extract data from zip file and put it in the project, then iteratively create directories "sim_res/path_count" and "mf_features/path_count" in directory "data/yelp-50k/exp_split/1/".
3. Create directory "log" in the project by "mkdir log".
4. Create directory "fm_res" in the project by "mkdir fm_res".

To generate the similarity matrices on yelp-50k dataset, run

python 200k_commu_mat_computation.py yelp-50k all 1

The arguments are explained in the following:

yelp-50k: specify the dataset.
all: run for all pre-defined meta-graphs.
1: run for the split dataset 1, i.e., exp_split/1

One dependent lib is bottleneck, you may install it with "pip install bottleneck".

To generate the latent features by MF based on the simiarity matrices, run

python mf_features_generator.py yelp-50k all 1

The arguments are the same as the above ones.

Note that, to improve the computation efficiency, some modules are implements with C and called in python(see load_lib method in mf.py). Thus to successfully run mf_features_generator.py, you need to compile two C source files. The following scripts are tested on CentOS, and readers may take as references.

gcc -fPIC --shared setVal.c -o setVal.so
gcc -fPIC --shared partXY.c -o partXY.so

After the compiling, you will get two files in the project directory "setVal.so" and "partXY.so".

After obtain the latent features, then the readers can run FMG model as following:

python run_exp.py config/yelp-50k.yaml -reg 0.5

One may read the comment in files in directory config for more information.

If you have any questions about this project, you may open issues, thus it can help more people who are interested in this project.