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DSL

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[IJCAI'2023] "DSL: Denoised Self-Augmented Learning for Social Recommendation"

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# Denoised Self-Augmented Learning for Social Recommendation (DSL)

[Tianle Wang](https://louiswng.github.io), [Lianghao Xia](https://akaxlh.github.io), [Chao Huang](https://sites.google.com/view/chaoh/home)*. (*Correspondence)

This is the PyTorch-based implementation for DSL model proposed in this paper:

> Denoised Self-Augmented Learning for Social Recommendation

![model](https://p.ipic.vip/ikwc4z.png)

## Abstract

Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling. Recently, Self-Supervised Learning (SSL) has proven to be remarkably effective in addressing data sparsity through augmented learning tasks. Inspired by this, researchers have attempted to incorporate SSL into social recommendation by supplementing the primary supervised task with social-aware self-supervised signals. However, social information can be unavoidably noisy in characterizing user preferences due to the ubiquitous presence of interest-irrelevant social connections, such as colleagues or classmates who do not share many common interests. To address this challenge, we propose a novel social recommender called the Denoised Self-Augmented Learning paradigm (DSL). Our model not only preserves helpful social relations to enhance user-item interaction modeling but also enables personalized cross-view knowledge transfer through adaptive semantic alignment in embedding space. Our experimental results on various recommendation benchmarks confirm the superiority of our DSL over state-of-the-art methods.

## Environment

The implementation for DSL is under the following development environment:

- python=3.8
- torch=1.12.1
- numpy=1.23.2
- scipy=1.9.1

## Datasets

Our experiments are conducted on three benchmark datasets collected from Ciao, Epinions and Yelp online platforms. In those sites, social connections can be established among users in addition to their observed implicit feedback (e.g., rating, click) over different items.

| Dataset  | # Users | # Items | # Interactions | Interaction Density | # Social Ties |
| :------: | :-----: | :-----: | :------------: | :-----------------: | :-----------: |
|   Ciao   |  6,672  | 98,875  |    198,181     |       0.0300%       |    109,503    |
| Epinions | 11,111  | 190,774 |    247,591     |       0.0117%       |    203,989    |
|   Yelp   | 161,305 | 114,852 |   1,118,645    |       0.0060%       |   2,142,242   |

## Usage

Please unzip the datasets first. Also you need to create the `History/` and the `Models/` directories. The command lines to train DSL on the three datasets are as below. The un-specified hyperparameters in the commands are set as default.

- Ciao

```shell
  bash scripts/run_ciao.sh
  ```

- Epinions

```shell
  bash scripts/run_epinions.sh
  ```

- Yelp

```shell
  bash scripts/run_yelp.sh
  ```

### Important Arguments

- `gnn_layer`: It is the number of gnn layers, which is searched from `{1, 2, 3, 4}`.
- `reg`: It is the weight for weight-decay regularization. We tune this hyperparameter from the set `{1e-4, 1e-5, 1e-6, 1e-7}`.
- `uuPre_reg`: It is the weight for social graph prediction regularization, which is tuned from `{1e1, 1e0, 1e-1, 1e-2, 1e-3}`
- `sal_reg`: It is the weight for self-augmented regularization. We tune it from the set `{1e-4, 1e-5, 1e-6}`

## Contact

For any question or feedback, feel free to contact [Tianle Wang](mailto:louis.wng@outlook.com).

## Citation

If you find DSL useful in your research or applications, please kindly cite:

```tex
@article{wang2023denoised,
  title={Denoised Self-Augmented Learning for Social Recommendation},
  author={Wang, Tianle and Xia, Lianghao and Huang, Chao},
  journal={arXiv preprint arXiv:2305.12685},
  year={2023}
}
```

DSL

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