This repository is the implementation of KGNN-LS (arXiv):

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang.
In Proceedings of The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019)

KGNN-LS applies the technique of graph neural networks (GNNs) to proces knowledge graphs for the purpose of recommendation. The model is enhanced by adding a label smoothness regularizer for more powerful and adaptive learning.

• 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 KGNN-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
  

• 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
    

• 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