Deep Knowledge-aware network

Author: shixiaowen03

.idea/workspace.xml

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+# 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)
+
+![](https://github.com/hwwang55/DKN/blob/master/framework.jpg)
+
+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)
+```