ssui-liu / learnable-embed-sizes-for-RecSys

A PyTorch implementation of ICLR 2021 paper: Learnable Embedding Sizes for Recommender Systems

Learnable Embedding Sizes for Recommender Systems

This repository contains PyTorch Implementation of ICLR 2021 paper: Learnable Embedding Sizes for Recommender Systems. Please check our paper for more details about our work if you are interested.

Usage

Following the steps below to run our codes:

1. Install torchfm

pip install torchfm

For more information about torchfm, please see:

https://github.com/rixwew/pytorch-fm

2. Download datasets

We provide MovieLens-1M dataset in data/ml-1m. If you want to run PEP on Criteo and Avazu datasets, you need to download the dataset at Criteo and Avazu.

3. Put the data in data/criteo or data/avazu

Raw data should be stored with the following file directory:

data/criteo/train.txt

data/avazu/train

4. Specify the hyper-parameters

For learning embedding sizes, the hyper-parameters are in train_[dataset].py

For retraining learned embedding sizes, the hyper-parameters are in train_[dataset]_retrain.py

5. Learning embedding sizes

Run train_[dataset].py to learn embedding sizes. Learned embedding will be saved in tmp/embedding/fm/[alias]/, named as number of parameters.

6. Retrain the pruned embedding

Run train_[dataset]_retrain.py to retrain the pruned embedding table. You need to specify what embedding table need to be retrain by hyper-parameter retrain_emb_param.

Requirements

• Python 3
• PyTorch 1.1.0

Citation

If you find this repo is useful for you, please kindly cite our paper.

@inproceedings{liu2021learnable,
    title={Learnable Embedding Sizes for Recommender Systems},
    author={Siyi Liu and Chen Gao and Yihong Chen and Depeng Jin and Yong Li},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=vQzcqQWIS0q}
}

Acknowledgment

The structure of this code is largely based on lambda-opt.