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 [email protected].