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https://github.com/hwwang55/DKN
A tensorflow implementation of DKN (Deep Knowledge-aware Network for News Recommendation)
https://github.com/hwwang55/DKN
knowledge-graph recommender-systems
Last synced: 3 months ago
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A tensorflow implementation of DKN (Deep Knowledge-aware Network for News Recommendation)
- Host: GitHub
- URL: https://github.com/hwwang55/DKN
- Owner: hwwang55
- License: mit
- Created: 2018-07-15T10:54:07.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-05-25T04:23:43.000Z (over 1 year ago)
- Last Synced: 2024-06-24T05:52:03.897Z (5 months ago)
- Topics: knowledge-graph, recommender-systems
- Language: C++
- Homepage:
- Size: 32.8 MB
- Stars: 406
- Watchers: 16
- Forks: 136
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - hwwang55/DKN
README
# DKN
This repository is the implementation of [DKN](https://dl.acm.org/citation.cfm?id=3186175) ([arXiv](https://arxiv.org/abs/1801.08284)):
> DKN: Deep Knowledge-Aware Network for News Recommendation
Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo
The Web Conference 2018 (WWW 2018)
DKN is a deep knowledge-aware network that takes advantage of knowledge graph representation in news recommendation.
The main components in DKN is a KCNN module and an attention module:
- The KCNN module is to learn from semantic-level and knowledge-level representations of news jointly.
The multiple channels and alignment of words and entities enable KCNN to combine information from heterogeneous sources.
- The attention module is to model the different impacts of a user’s diverse historical interests on current candidate news.
### Files in the folder
- `data/`
- `kg/`
- `Fast-TransX`: an efficient implementation of TransE and its extended models for Knowledge Graph Embedding (from https://github.com/thunlp/Fast-TransX);
- `kg.txt`: knowledge graph file;
- `kg_preprocess.py`: pre-process the knowledge graph and output knowledge embedding files for DKN;
- `prepare_data_for_transx.py`: generate the required input files for Fast-TransX;
- `news/`
- `news_preprocess.py`: pre-process the news dataset;
- `raw_test.txt`: raw test data file;
- `raw_train.txt`: raw train data file;
- `src/`: implementations of DKN.
> Note: Due to the pricacy policies of Bing News and file size limits on Github, the released raw dataset and the knowledge graph in this repository is only a small sample of the original ones reported in the paper.
### Format of input files
- **raw_train.txt** and **raw_test.txt**:
`user_id[TAB]news_title[TAB]label[TAB]entity_info`
for each line, where `news_title` is a list of words `w1 w2 ... wn`, and `entity_info` is a list of pairs of entity id and entity name: `entity_id_1:entity_name;entity_id_2:entity_name...`
- **kg.txt**:
`head[TAB]relation[TAB]tail`
for each line, where `head` and `tail` are entity ids and `relation` is the relation id.
### Required packages
The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):
- tensorflow-gpu == 1.4.0
- numpy == 1.14.5
- sklearn == 0.19.1
- pandas == 0.23.0
- gensim == 3.5.0
### Running the code
```
$ cd data/news
$ python news_preprocess.py
$ cd ../kg
$ python prepare_data_for_transx.py
$ cd Fast-TransX/transE/ (note: you can also choose other KGE methods)
$ g++ transE.cpp -o transE -pthread -O3 -march=native
$ ./transE
$ cd ../..
$ python kg_preprocess.py
$ cd ../../src
$ python main.py (note: use -h to check optional arguments)
```