Adaptive Denoising Training (ADT) for Recommendation.

Related tags

Deep Learning DenoisingRec
Overview

DenoisingRec

Adaptive Denoising Training for Recommendation.

This is the pytorch implementation of our paper at WSDM 2021:

Denoising Implicit Feedback for Recommendation.
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua.

Environment

  • Anaconda 3
  • python 3.7.3
  • pytorch 1.4.0
  • numpy 1.16.4

For others, please refer to the file env.yaml.

Usage

Training

T_CE

python main.py --dataset=$1 --model=$2 --drop_rate=$3 --num_gradual=$4 --gpu=$5

or use run.sh

sh run.sh dataset model drop_rate num_gradual gpu_id

The output will be in the ./log/xxx folder.

R_CE

sh run.sh dataset model alpha gpu_id

Inference

We provide the code to inference based on the well-trained model parameters.

python inference.py --dataset=$1 --model=$2 --drop_rate=$3 --num_gradual=$4 --gpu=$5

Examples

  1. Train GMF by T_CE on Yelp:
python main.py --dataset=yelp --model=GMF --drop_rate=0.1 --num_gradual=30000 --gpu=0
  1. Train NeuMF by R_CE on Amazon_book
python main.py --dataset=amazon_book --model=NeuMF-end --alpha=_0.25 --gpu=0

We release all training logs in ./log folder. The hyperparameter settings can be found in the log file. The well-trained parameter files are too big to upload to Github. I will upload to drives later and share it here.

Citation

If you use our code, please kindly cite:

@article{wang2020denoising,
  title={Denoising Implicit Feedback for Recommendation},
  author={Wang, Wenjie and Feng, Fuli and He, Xiangnan and Nie, Liqiang and Chua, Tat-Seng},
  journal={arXiv preprint arXiv:2006.04153},
  year={2020}
}

Acknowledgment

Thanks to the NCF implementation:

Besides, this research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative, and the National Natural Science Foundation of China (61972372, U19A2079). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.

License

NUS © NExT++

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Comments
  • Dataset preprocessing

    Dataset preprocessing

    Hi, thanks for your great work. I'm very interested in your paper. Could you please provide the dataset preprocessing codes for generating the uploaded data? If not, could you give some step-by-step descriptions? Thanks for any possible help.

    opened by guosyjlu 5
  • 作者您好。关于epoch的问题想要咨询一下您。

    作者您好。关于epoch的问题想要咨询一下您。

    作者您好。我运行了你开源的代码，有一些疑惑想要咨询一下您。 1、我在Yelp数据集上运行了GMF+T-CE模型，具体的参数设置是：batch_size=1024, dataset='yelp', drop_rate=0.2, dropout=0.0, epochs=10, eval_freq=2000, exponent=1, factor_num=32, gpu='3', lr=0.001, model='GMF', num_gradual=30000, num_layers=3, num_ng=1, out=True, top_k=[50, 100]，这些都是默认的设置，我仅仅改变了训练次数epoch，即epoch =[10, 20, 30]，训练10个epoch时，Recall=[0.0875, 0.1452], NDCG=[0.0357,0.0487]； 训练20个epoch时，Recall=[0.0941，0.1550], NDCG=[0.0382，0.0520]；训练30个epoch时，Recall=[0.1021，0.1649], NDCG=[0.0420，0.0562]。可以看到性能还是一直在大幅上升状态，即训练10个epoch并没有让模型达到收敛状态。我观察了论文中实验结果表3，其中的实验结果与epoch=10时的基本一致，这样的对比是否有失公允呢？

    2、虽然代码中设置了固定的随机数种子，但每次实验结果似乎都不同，并且和论文中的结果对不上，是因为多次取平均的结果吗？

    非常感谢您在百忙之中阅读我的问题，期待您的回复。 @WenjieWWJ

    opened by BlueGhostZ 4
  • About evaluation on Recall

    About evaluation on Recall

    Hi, when I perform "python inference.py --dataset=yelp --model=GMF --drop_rate=0.1 --num_gradual=30000 --gpu=0", I obtain Recall@50 and Recall@100 both at 0.0000 though NDCG is the same as that of the paper. Is there something wrong in your source code?

    opened by Guanyu-Lin 2
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