文件列表:

Basic-DKN-Demo (0, 2019-04-27)
Basic-DKN-Demo\data_loader.py (1995, 2019-04-27)
Basic-DKN-Demo\dkn.py (8103, 2019-04-27)
Basic-DKN-Demo\kg (0, 2019-04-27)
Basic-DKN-Demo\kg\entity2id.txt (413986, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX (0, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\LICENSE (1062, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transD (0, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transD\transD (56240, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transD\transD.cpp (15009, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transE (0, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transE\transE (48832, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transE\transE.cpp (9920, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transH (0, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transH\transH.cpp (13046, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transR (0, 2019-04-27)
Basic-DKN-Demo\kg\Fast-TransX\transR\transR.cpp (14337, 2019-04-27)
Basic-DKN-Demo\kg\kg.txt (10686463, 2019-04-27)
Basic-DKN-Demo\kg\kg_preprocess.py (2470, 2019-04-27)
Basic-DKN-Demo\kg\prepare_data_for_transx.py (2334, 2019-04-27)
Basic-DKN-Demo\main.py (2046, 2019-04-27)
Basic-DKN-Demo\news (0, 2019-04-27)
Basic-DKN-Demo\news\news_preprocess.py (5992, 2019-04-27)
Basic-DKN-Demo\news\raw_test.txt (39532, 2019-04-27)
Basic-DKN-Demo\news\raw_train.txt (884718, 2019-04-27)
Basic-DKN-Demo\train.py (1313, 2019-04-27)

# 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 users 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 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 ../ $ python main.py (note: use -h to check optional arguments) ```

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