This repository is the implementation of MKR (arXiv):

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo.
In Proceedings of The 2019 Web Conference (WWW 2019)

MKR is a Multi-task learning approach for Knowledge graph enhanced Recommendation. MKR consists of two parts: the recommender system (RS) module and the knowledge graph embedding (KGE) module. The two modules are bridged by cross&compress units, which can automatically learn high-order interactions of item and entity features and transfer knowledge between the two tasks.

• data/
  • book/
    • BX-Book-Ratings.csv: raw rating file of Book-Crossing dataset;
    • 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;
  • 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;
    • ratrings.dat: raw rating file of MovieLens-1M;
  • 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 MKR.

• Movie

  $ cd src
  $ python preprocess.py --dataset movie
  $ python main.py
  

• Book

  • $ cd src
    $ python preprocess.py --dataset book
    

  • open main.py file;

  • comment the code blocks of parameter settings for MovieLens-1M;

  • uncomment the code blocks of parameter settings for Book-Crossing;

  • $ python main.py
    

• Music

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

  • open main.py file;

  • comment the code blocks of parameter settings for MovieLens-1M;

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

  • $ python main.py