Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network

Introduction

This repo is an implimentation of CIKM2020 paper Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network.

Dependencies

python 3.7

pytorch 1.2.0

torch geometric

numpy

pandas

h5py

tensorboardX

setproctitle

GPUtil

matplotlib

Dataset

We use Yelp dataset provided in HIN-Datasets-for-Recommendation-and-Network-Embedding. You can star this dataset repo as you like, and in this repo we already integrated necessary data.

Here, we use the following node types of Yelp dataset to generate our HIN. The bolded relation is the main relation.

Relations (A-B)	#A	#B	#A-B
User-Business (U-B)	16239	14284	198397
User-User (U-U)	16239	16239	158590
User-Compliment (U-O)	16239	11	76875
Business-City (B-I)	14284	47	14267
Business-Category (B-I)	14284	511	40009

Usage

0. Data Process (You do not need to perform this step)

   cd data_process
   python trans_data_yelp.py
   python BMF.py

   trans_data_yelp.py will generate a dataset file which is needed for GEMS. We already generated this dataset file and put it under the main directory called yelp_dataset.hdf5.

   BMF.py is one of the baseline, and it's also performs as a pre-train embedding generator. The pre-train embeddings are located under MF_pretrain directory.

1. Train the model

   mkdir result_log
   mkdir result_log/yelp
   mkdir result_edges
   mkdir error_genes_results
   python GEMS_yelp.py

   Note that you may need to change the multi-process training setup according to your server. Search #IMPORTANT in GEMS_yelp.py to locate multi-process setup.

2. Try to use predictor when you got some results

 python GEMS_yelp_with_predictor.py

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