https://github.com/hwwang55/KGCN

A tensorflow implementation of Knowledge Graph Convolutional Networks
https://github.com/hwwang55/KGCN

graph-convolutional-networks knowledge-graph recommender-systems

Last synced: about 2 months ago
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A tensorflow implementation of Knowledge Graph Convolutional Networks

• Host: GitHub
• URL: https://github.com/hwwang55/KGCN
• Owner: hwwang55
• License: mit
• Created: 2018-07-17T14:38:27.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-01-09T23:58:28.000Z (about 1 year ago)
• Last Synced: 2024-08-09T13:18:37.312Z (5 months ago)
• Topics: graph-convolutional-networks, knowledge-graph, recommender-systems
• Language: Python
• Homepage:
• Size: 171 MB
• Stars: 472
• Watchers: 8
• Forks: 151
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# KGCN

This repository is the implementation of [KGCN](https://dl.acm.org/citation.cfm?id=3313417) ([arXiv](https://arxiv.org/abs/1904.12575)):

> 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)

![](https://github.com/hwwang55/KGCN/blob/master/framework.png)

KGCN is **K**nowledge **G**raph **C**onvolutional **N**etworks 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

    ```