KGCN

This repository is the implementation of KGCN (arXiv):

Knowledge Graph Convolutional Networks for Recommender Systems
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo.
In Proceedings of The 2019 Web Conference (WWW 2019)

KGCN is Knowledge Graph Convolutional Networks for recommender systems, which uses the technique of graph convolutional networks (GCN) to proces knowledge graphs for the purpose of recommendation.

Files in the folder

• data/
  • movie/
    • item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG;
    • kg.txt: knowledge graph file;
  • music/
    • item_index2entity_id.txt: the mapping from item indices in the raw rating file to entity IDs in the KG;
    • kg.txt: knowledge graph file;
    • user_artists.dat: raw rating file of Last.FM;
• src/: implementations of KGCN.

Running the code

• Movie
  (The raw rating file of MovieLens-20M is too large to be contained in this repository. Download the dataset first.)

  $ wget http://files.grouplens.org/datasets/movielens/ml-20m.zip
  $ unzip ml-20m.zip
  $ mv ml-20m/ratings.csv data/movie/
  $ cd src
  $ python preprocess.py -d movie
  

• Music

  • $ cd src
    $ python preprocess.py -d music
    

  • open src/main.py file;

  • comment the code blocks of parameter settings for MovieLens-20M;

  • uncomment the code blocks of parameter settings for Last.FM;

  • $ python main.py