OpenHGNN/openhgnn/output/KGCN

• KGCN[WWW2020]
• • How to run
  • Performance: Recommendation
  • Dataset
  • • Description
  • TrainerFlow: Recommendation
  • • • model
  • More
  • • • Contributor
      • If you have any questions,

KGCN[WWW2020]

• paper: Knowledge Graph Convolutional Networks for Recommender Systems
• Code from author: KGCN

How to run

• Clone the Openhgnn-DGL

  python main.py -m KGCN -d LastFM4KGCN -t recommendation -g 0 --use_best_config
  

  If you do not have gpu, set -gpu -1.

  the dataset Last.FM is supported.

Performance: Recommendation

• Device: GPU, GeForce RTX 3090
• Dataset: Last.FM

Recommendation	AUC	F1
KGCN-sum	paper: 79.4% OpenHGNN: 79.6%	paper: 71.9% OpenHGNN: 71.8%
KGCN-concat	paper: 79.6% OpenHGNN: 78.9%	paper: 72.1% OpenHGNN: 71.4%
KGCN-neighbor	paper: 78.1% OpenHGNN: 78.6%	paper: 69.9% OpenHGNN: 71.0%

Dataset

• We process the KGCN dataset given by KGCN. It saved as dgl.heterograph and can be loaded by dgl.load_graphs

Description

• Last.FM

  	Last.FM
  User	1872
  item	3846
  interactions	42346
  entities	9366
  relations	60
  KG triples	15518

TrainerFlow: Recommendation

model

• ​ KGCN
  • ​ KGCN is to aggregate the entity representation and its neighborhood representation

More

Contributor

Yanhu Mo[GAMMA LAB]

If you have any questions,

Submit an issue or email to myth@bupt.edu.cn.