https://github.com/jrzaurin/neural_cf
Comparing keras, pytorch and gluon using neural collaborative filtering
https://github.com/jrzaurin/neural_cf
collaborative-filtering deep-learning gluon keras pytorch recommender-system
Last synced: 3 months ago
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Comparing keras, pytorch and gluon using neural collaborative filtering
- Host: GitHub
- URL: https://github.com/jrzaurin/neural_cf
- Owner: jrzaurin
- Created: 2018-10-15T13:32:15.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-28T14:53:10.000Z (over 6 years ago)
- Last Synced: 2025-04-14T07:56:50.473Z (6 months ago)
- Topics: collaborative-filtering, deep-learning, gluon, keras, pytorch, recommender-system
- Language: Jupyter Notebook
- Homepage:
- Size: 53.8 MB
- Stars: 18
- Watchers: 5
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Neural Collaborative Filtering with Keras, Pytorch and Gluon
This repo contains an implementation of [Xiangnan He, et al,
2017](https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf) neural
collaborative filtering in `Keras` (original paper), `Gluon` and `Pytorch`.
The `Keras` code is mostly borrowed from the author's original
[repo](https://github.com/hexiangnan/neural_collaborative_filtering), adapted
to the new `keras 2.2` API and `python 3`. Of course, I strongly recommend
reading their [paper](https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf).
Everything one needs to run the experiment is in this repo. The code is
organized as follows:
1. The core of the repo are of course the `GMF_DLFRAME.py`,
`MLP_DLFRAME.py` and `NeuMF_DLFRAME.py` where `DLFRAME` is `keras`,
`pytorch` and `gluon`
2. I have also included `data_preparation.py` and `data_comparison.ipynb`. The
first shows how to prepare the data for the experiment (not included in the
author's original
[repo](https://github.com/hexiangnan/neural_collaborative_filtering)) and the
second simply shows that the results of my data preparation and those of
Xiangnan He are consistent.
3. If you are just interested in a comparison between the results obtained
with `Keras`, `Pytorch` and `Gluon`, you can directly go to
`results_summary.ipynb`.
All the experiments run are included in `run_net.sh`. If you clone this repo you could directly copy and paste the content in that file. For example, the following line will run a GMF model using `Gluon`, with batch_size 256, learning rate 0.01, 32 dim embeddings for 30 epochs:
`python GMF_gluon.py --batch_size 256 --lr 0.01 --n_emb 32 --epochs 30`
The best performing GMF and MLP models are included in the dir `models`.
Given the relative simplicity of the model, I thought this would be a good
exercise to illustrate the similarities and differences between the 3 frames .
In addition the results obtained turned out to be quite interesting.
The Figure below shows the *Hit Ratio* (HR) and *Normalized Discounted
Cumulative Gain* (NDCG) at k=10 for the MLP, GMF models and also the training
time for the MLP model.
Top: *Hit Ratio* (HR) and *Normalized Discounted Cumulative Gain* (NDCG) at
k=10 for both the GMF and MLP models vs the number of embeddings. Bottom:
training time for the GMF and MLP models per batch size and number of embeddings respectively.
For more details, go to `results_summary.ipynb`
Any suggestion, email me at: jrzaurin@gmail.com