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https://github.com/hexiangnan/neural_collaborative_filtering
Neural Collaborative Filtering
https://github.com/hexiangnan/neural_collaborative_filtering
collaborative-filtering deep-learning recommender-system
Last synced: 25 days ago
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Neural Collaborative Filtering
- Host: GitHub
- URL: https://github.com/hexiangnan/neural_collaborative_filtering
- Owner: hexiangnan
- License: apache-2.0
- Created: 2017-02-02T02:55:58.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-08-27T06:26:07.000Z (about 2 years ago)
- Last Synced: 2024-10-01T13:01:21.079Z (about 1 month ago)
- Topics: collaborative-filtering, deep-learning, recommender-system
- Language: Python
- Homepage:
- Size: 23.2 MB
- Stars: 1,790
- Watchers: 31
- Forks: 653
- Open Issues: 53
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Collaborative Filtering
This is our implementation for the paper:
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua (2017). [Neural Collaborative Filtering.](http://dl.acm.org/citation.cfm?id=3052569) In Proceedings of WWW '17, Perth, Australia, April 03-07, 2017.
Three collaborative filtering models: Generalized Matrix Factorization (GMF), Multi-Layer Perceptron (MLP), and Neural Matrix Factorization (NeuMF). To target the models for implicit feedback and ranking task, we optimize them using log loss with negative sampling.
**Please cite our WWW'17 paper if you use our codes. Thanks!**
Author: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)
## Environment Settings
We use Keras with Theano as the backend.
- Keras version: '1.0.7'
- Theano version: '0.8.0'
## Example to run the codes.
The instruction of commands has been clearly stated in the codes (see the parse_args function).
Run GMF:
```
python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
```
Run MLP:
```
python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
```
Run NeuMF (without pre-training):
```
python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
```
Run NeuMF (with pre-training):
```
python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrain/ml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrain/ml-1m_MLP_[64,32,16,8]_1501652038.h5
```
Note on tuning NeuMF: our experience is that for small predictive factors, running NeuMF without pre-training can achieve better performance than GMF and MLP. For large predictive factors, pre-training NeuMF can yield better performance (may need tune regularization for GMF and MLP).
## Docker Quickstart
Docker quickstart guide can be used for evaluating models quickly.
Install Docker Engine
- [Ubuntu Installation](https://docs.docker.com/engine/installation/linux/ubuntu/)
- [Mac OSX Installation](https://docs.docker.com/docker-for-mac/install/)
- [Windows Installation](https://docs.docker.com/docker-for-windows/install/)
Build a keras-theano docker image
```
docker build --no-cache=true -t ncf-keras-theano .
```
### Example to run the codes with Docker.
Run the docker image with a volume (Run GMF):
```
docker run --volume=$(pwd):/home ncf-keras-theano python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
```
Run the docker image with a volume (Run MLP):
```
docker run --volume=$(pwd):/home ncf-keras-theano python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
```
Run the docker image with a volume (Run NeuMF -without pre-training):
```
docker run --volume=$(pwd):/home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
```
Run the docker image with a volume (Run NeuMF -with pre-training):
```
docker run --volume=$(pwd):/home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrain/ml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrain/ml-1m_MLP_[64,32,16,8]_1501652038.h5
```
* **Note**: If you are using `zsh` and get an error like `zsh: no matches found: [64,32,16,8]`, should use `single quotation marks` for array parameters like `--layers '[64,32,16,8]'`.
### Dataset
We provide two processed datasets: MovieLens 1 Million (ml-1m) and Pinterest (pinterest-20).
train.rating:
- Train file.
- Each Line is a training instance: userID\t itemID\t rating\t timestamp (if have)
test.rating:
- Test file (positive instances).
- Each Line is a testing instance: userID\t itemID\t rating\t timestamp (if have)
test.negative
- Test file (negative instances).
- Each line corresponds to the line of test.rating, containing 99 negative samples.
- Each line is in the format: (userID,itemID)\t negativeItemID1\t negativeItemID2 ...
Last Update Date: December 23, 2018