https://github.com/weberrr/CKAN
https://github.com/weberrr/CKAN
Last synced: 10 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/weberrr/CKAN
- Owner: weberrr
- Created: 2020-07-20T01:28:08.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-25T10:57:24.000Z (almost 2 years ago)
- Last Synced: 2024-11-28T01:34:36.642Z (over 1 year ago)
- Language: Python
- Size: 8 MB
- Stars: 43
- Watchers: 2
- Forks: 13
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - weberrr/CKAN - aware Attentive Network for Recommender Systems 协作知识感知的注意力网络推荐系统 (其他_推荐系统 / 网络服务_其他)
README
# CKAN
This repository is the implementation of CKAN :
> CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems，SIGIR 2020
>
> Ze Wang, Guangyan Lin, Huobin Tan, Qinghong Chen, and Xiyang Liu
## Required packages
The code has been tested running under Python 3.6.9, with the following packages installed (along with their dependencies):
- torch==1.3.0
- torchvision==0.4.1
- numpy==1.17.3
- scikit-learn==0.21.3
## Files in the folder
- `data/`
- `music/`
- `user_artists.dat`: raw rating file of Last.FM dataset;
- `kg.txt`: knowledge graph file;
- `item_index2entity_id.txt`: the mapping from item indices in the raw rating file to entity IDs in the KG;
- `book/` ( the structure of other datasets is similar )
- `movie/`
- `restaurant/`
- `src/`: implementations of CKAN.
## Perpare & preprocess data
We have prepared processed data in `music` and `book` . You can skip this step and proceed directly to the next step. But for larger dataset ( `movie` and `restaurant` ), you need to download and preprocess yourself by following the steps below:
- Music
```
$ wget http://files.grouplens.org/datasets/hetrec2011/hetrec2011-lastfm-2k.zip
$ unzip hetrec2011-lastfm-2k.zip
$ mv hetrec2011-lastfm-2k/user_artists.dat data/music/
$ cd src
$ python preprocess.py --dataset music
$ python main.py
```
- Book
```
$ wget http://www2.informatik.uni-freiburg.de/~cziegler/BX/BX-CSV-Dump.zip
$ unzip BX-CSV-Dump.zip
$ mv BX-CSV-Dump/BX-Book-Ratings.csv data/book/
$ cd src
$ python preprocess.py --dataset book
```
- movie
```
$ 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 --dataset movie
```
- Restaurant
```
$ wget https://github.com/hwwang55/KGNN-LS/raw/master/data/restaurant/Dianping-Food.zip
$ unzip Dianping-Food.zip
$ mv Dianping-Food/ data/restaurant
```
## Run the code
We set a random seed to facilitate users to observe the effect of the model easily. You can reset the random seed by adding parameters this way: `--random_flag True`
- music
```
$ cd src
$ python main.py --dataset music (note: use -h to check optional arguments)
```
- book
```
$ cd src
$ python main.py --dataset book --n_layer 2 --user_triple_set_size 16
```
- movie
```
$ cd src
$ python main.py --dataset movie --n_layer 1 --user_triple_set_size 32
```
- restaurant
```
$ cd src
$ python main.py --dataset restaurant --n_layer 1 --user_triple_set_size 16
```