This repository is the implementation of KGCN and KGCN-LS.

• 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;
  • restaurant/
    • Dianping-Food.zip: containing the final rating file and the final KG file;
• src/: implementations of KGCN and KGCN-LS.

• 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 --dataset movie
  $ python main.py (settings for KGCN and KGCN-LS are different. Plase carefully choose the corresponding code blocks in main.py)
  

• Music

  • $ cd src
    $ python preprocess.py --dataset 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 (settings for KGCN and KGCN-LS are different. Plase carefully choose the corresponding code blocks in main.py)
    

• Restaurant

  $ cd data/restaurant
  $ unzip Dianping-Food.zip
  

  • open src/main.py file;

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

  • uncomment the code blocks of parameter settings for Dianping-Food;

  • $ python main.py (settings for KGCN and KGCN-LS are different. Plase carefully choose the corresponding code blocks in main.py)