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Improving Recommendation Fairness via Data Augmentation-WWW23

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# FDA

WWW 2023 Conference

Paper title: Improving Recommendation Fairness via Data Augmentation [arxiv](https://arxiv.org/abs/2302.06333) [WWW](https://dl.acm.org/doi/abs/10.1145/3543507.3583341)

## Prerequisites

- PyTorch 1.7
- Python 3.5
- NVIDIA GPU + CUDA CuDNN

## Getting Started

### Installation

- Clone this repo

### Train/test

- Train FDA_BPR on MovieLens:

```bash
cd FDA_anonymous/fda_bpr_ml
python main.py
```

- Train FDA_GCCF on MovieLens:

```bash
cd FDA_anonymous/fda_gccf_ml
python main.py
```

- Train FDA_BPR on LastFM:

```bash
cd FDA_anonymous/fda_bpr_lastfm
python main.py
```

- Train FDA_GCCF on LastFM:

```bash
cd FDA_anonymous/fda_gccf_lastfm
python main.py
```

**Note**: The results of FDA will be output on the terminal after the training.

**ERROR**: 
1. The error " 'weight' must be 2-D" occurred due to inconsistent versions of the Pytorch version.

- Solution:
> ```
> gender = F.embedding(u_batch,self.users_features)
> male_gender = gender.type(torch.BoolTensor)
> female_gender = (1-gender).type(torch.BoolTensor)
> ```
> Replace the above code with the following code:
> ```
> gender = F.embedding(u_batch,torch.unsqueeze(self.users_features,1)).reshape(-1)
> male_gender = gender.type(torch.BoolTensor).cuda()
> female_gender = (1-gender).type(torch.BoolTensor).cuda()        
> ```

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