MoHINRec

The code for CIKM 19 paper "Motif Enhanced Recommendation over Heterogeneous Information Network"

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

@inproceedings{zhao2019mohinrec,
title={Motif Enhanced Recommendation over Heterogeneous Information Network},
author={Zhao, Huan and Zhou, Yingqi and Song, Yangqiu and Lee, Dik Lun},
booktitle={CIKM},
year={2019}
}

We use Epinions Dataset and CiaoDVD Datasets from https://www.cse.msu.edu/~tangjili/trust.html and https://www.librec.net/datasets.html, respectively. Any problems, you can create an issue.

Instructions

For the sake of ease, a quick instruction is given for readers to reproduce the whole process on Epinions dataset. Note that the programs are testd on Linux(CentOS release 6.9), Python 2.7 and Numpy 1.14.0 from Anaconda 4.3.6.

Prerequisites

1. Create a directory "data" in this project directory, download epinions dataset and put it under "data/" .
2. Create directory "log" in the project by "mkdir log".
3. Create directory "fm_res" in the project by "mkdir fm_res".
4. Open preprocess_E.py in this project directory, set the value of "dir_" equals "data/epinions/", and then run

python preprocess_E.py

5. Iteratively create directories "sim_res/path_count" and "mf_features/path_count" in directory "data/epinions/exp_split/1/".

Meta-graph Similarity Matrices Computation.

To generate the MoHINRec M1-M7 similarity matrices with alpha from 0 to 1 on Epinions dataset, run

python e_commu_mat_computation.py epinions 1

The arguments are explained in the following:

epinions: specify the dataset.
1: run for the split dataset 1, i.e., exp_split/1

This command generates MoHINRec M1-M7 similarity matrices with alpha from 0 to 1. One dependent lib is bottleneck, you may install it with "pip install bottleneck".

Meta-graph Latent Features Generation.

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

python mf_features_generator.py epinions 1

This command generates the latent features for MoHINRec M1-M7 similarity matrices. 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".

FMG

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

python run_exp.py config/epinions.yaml -reg 0.5

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

Misc

If you have any questions about this project, you can open issues, thus it can help more people who are interested in this project. I will reply to your issues as soon as possible.