https://github.com/hwwang55/MKR

A tensorflow implementation of MKR (Multi-task Learning for Knowledge Graph Enhanced Recommendation)
https://github.com/hwwang55/MKR

knowledge-graph multi-task-learning recommender-systems

Last synced: 26 days ago
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A tensorflow implementation of MKR (Multi-task Learning for Knowledge Graph Enhanced Recommendation)

• Host: GitHub
• URL: https://github.com/hwwang55/MKR
• Owner: hwwang55
• License: mit
• Created: 2018-08-10T15:00:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-11-22T02:09:13.000Z (about 5 years ago)
• Last Synced: 2024-08-04T21:08:00.016Z (4 months ago)
• Topics: knowledge-graph, multi-task-learning, recommender-systems
• Language: Python
• Homepage:
• Size: 14.3 MB
• Stars: 319
• Watchers: 10
• Forks: 110
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• graph-networks - MKR (multi-task learning for knowledge graph enhanced recommendation)

README

        
# MKR

This repository is the implementation of MKR ([arXiv](https://arxiv.org/abs/1901.08907)):

> 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)

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

MKR is a **M**ulti-task learning approach for **K**nowledge graph enhanced **R**ecommendation.

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.

### Files in the folder

- `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.

### Running the code

- 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

    ```