https://github.com/yihong-chen/neural-collaborative-filtering

pytorch version of neural collaborative filtering
https://github.com/yihong-chen/neural-collaborative-filtering

collaborative-filtering deep-learning matrix-factorization python recommender-systems

Last synced: 3 months ago
JSON representation

pytorch version of neural collaborative filtering

• Host: GitHub
• URL: https://github.com/yihong-chen/neural-collaborative-filtering
• Owner: yihong-chen
• Created: 2018-03-16T13:31:30.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-05-05T05:19:05.000Z (6 months ago)
• Last Synced: 2024-05-18T20:43:32.730Z (6 months ago)
• Topics: collaborative-filtering, deep-learning, matrix-factorization, python, recommender-systems
• Language: Jupyter Notebook
• Homepage:
• Size: 14 MB
• Stars: 449
• Watchers: 8
• Forks: 144
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-drug-discovery - [Python Reference(Pytorch)

README

        
# neural-collaborative-filtering

Neural collaborative filtering(NCF), is a deep learning based framework for making recommendations. The key idea is to learn the user-item interaction using neural networks. Check the follwing paper for details about NCF.

> He, Xiangnan, et al. "Neural collaborative filtering." Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017.

The authors of NCF actually published [a nice implementation](https://github.com/hexiangnan/neural_collaborative_filtering) written in tensorflow(keras). This repo instead provides my implementation written in **pytorch**. I hope it would be helpful to pytorch fans. Have fun playing with it!

## Run!

```bash

python train.py

```

modify the config in `train.py` to change the hyper-parameters.

## Dataset

[The Movielens 1M Dataset](http://grouplens.org/datasets/movielens/1m/) is used to test the repo.

## Files

> `data.py`: prepare train/test dataset

>

> `utils.py`: some handy functions for model training etc.

>

> `metrics.py`: evaluation metrics including hit ratio(HR) and NDCG

>

> `gmf.py`: generalized matrix factorization model

>

> `mlp.py`: multi-layer perceptron model

>

> `neumf.py`: fusion of gmf and mlp

>

> `engine.py`: training engine

>

> `train.py`: entry point for train a NCF model

## Performance

The hyper params are not tuned. Better performance can be achieved with careful tuning, especially for the MLP model. Pretraining the user embedding & item embedding might be helpful to improve the performance of the MLP model. 

Experiments' results with `num_negative_samples = 4` and `dim_latent_factor=8`  are shown as follows

![GMF V.S. MLP](./res/figure/factor8neg4.png)

Note that the MLP model was trained from scratch but the authors suggest that the performance might be boosted by pretrain the embedding layer with GMF model.

![NeuMF pretrain V.S no pretrain](./res/figure/neumf_factor8neg4.png)

The pretrained version converges much faster.

### L2 regularization for GMF model

Large l2 regularization might lead to the bug of  `HR=0.0 NDCG=0.0`

### L2 regularization for MLP model

a bit l2 regulzrization seems to improve the performance of the MLP model

![L2 for MLP](./res/figure/mlp_l2_reg.png)

### MLP with pretrained user/item embedding

Pre-training the MLP model with user/item embedding from the trained GMF gives better result.

MLP network size = [16, 64, 32, 16, 8]

![Pretrain for MLP](./res/figure/mlp_pretrain_hr.png)

![Pretrain for MLP](./res/figure/mlp_pretrain_ndcg.png)

### Implicit feedback without pretrain

Ratings are set to 1 (interacted) or 0 (uninteracted). Train from scratch.

![binarize](./res/figure/binarize.png) 

## CPU training

The code can also run on CPUs and actually pretty fast for small datasets.

## Requirements

The repo works under torch 1.0 (gpu&cpu) and torch 2.3.1(cpu, gpu yet to be tested). You can find the old versions in **tags**.