Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks

Related tags

Deep LearningSHGCN
Overview

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks

This is our Pytorch implementation for the paper:

Zirui Zhu, Chen Gao, Xu Chen, Nian Li, Depeng Jin, and Yong Li. Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks, in ICDE 2022.

Introduction

Social HyperGraph Convolutionl Network (SHGCN) is a new recommendation framework based on hypergraph convolution, effectively utilizing the triple social relations.

Environment Requirement

The code has been tested under Python 3.6.10. The required packages are as follows:

  • Pytorch == 1.6.0
  • numpy == 1.19.1
  • scipy == 1.5.2
  • pandas == 1.1.1

Example to Run the Codes

  • Beidian dataset
python main.py --dataset Beidian --model SHGCN --gpu 0 --emb_dim 32 --num_layer 2 --epoch 500 --batch_size 4096
  • Beibei dataset
python main.py --dataset Beibei --model SHGCN --gpu 0 --emb_dim 32 --num_layer 2 --epoch 500 --batch_size 4096

Dataset

There are two datasets released here. Each contains four files.

  • data.train

    • Training set.
    • Each line contains a purchase log, which can be represented as:
      • (user ID, item ID)
  • data.val

    • Validation set.
    • Each line contains a purchase log, which can be represented as:
      • (user ID, item ID)
  • data.test

    • Testing set.
    • Each line contains a purchase log, which can be represented as:
      • (user ID, item ID)
  • social.share

    • Social interactions logs.
    • Each line contains a triplet. It can be represented uniformly as
      • (user1 ID, user2 ID, item ID)
    • In the Beidian dataset, each triplet represents a social sharing behavior.
    • In the Beibei dataset, each triplet represents a group buying behavior.
Owner
Zirui Zhu
All good things take their time.
Zirui Zhu
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