Recommendation_KuaiRand

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Recommendation_KuaiRand

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基于KuaiRand数据集的推荐算法应用实践

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# Recommendation_KuaiRand

## KuaiRand场景中多目标CVR预测的任务特化专家优化实践

本项目基于 KuaiRand 数据集，构建用于点击、点赞、关注、评论等多任务反馈预测的推荐系统模型。当前已完成数据加载与样本拼接的基础处理流程，以及MMoE基本框架。

---

## 📁 项目结构
```
Recommendation\_KuaiRand/
├── KuaiRand-Pure/
│   ├── data/
│   │   ├── log_standard_4_08_to_4_21_pure.csv
│   │   ├── log_standard_4_22_to_5_08_pure.csv
│   │   ├── user_features_pure.csv
│   │   ├── video_features_basic_pure.csv
│   │   └── video_features_statistic_pure.csv
│   ├── data_processed/
│   └── saved/
├── data_process.py                        # 数据预处理脚本
├── main.py                                # 模型训练评估
├── MMoE_model.py                          # MMoE网络模型
└── README.md
```
---

## ✅ 已完成工作

- [x] 加载行为日志、用户特征、视频特征数据
- [x] 将用户和视频特征合并至行为数据，生成训练样本
- [x] 特征处理（缺失值填充、编码、归一化等）
- [x] 构建 CVR 预测模型（点击/点赞/关注/评论等）
- [ ] 模型评估与优化

---

## 🚀 使用说明

1. 确保将原始数据放置在 `KuaiRand-Pure/data/` 目录下；
2. 运行数据预处理（样本拼接、缺失值处理、映射编码、归一化）脚本：

```bash
python data_process.py
````

3. 预处理数据结果将保存在：

```
KuaiRand-Pure/data_processed/
```

4. MMoE模型训练评估：

```
python main.py
```

---

## 📚 数据集引用

本项目使用的 KuaiRand 数据集来自 CIKM 2022：

```bibtex
@inproceedings{gao2022kuairand,
  title = {KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos},
  author = {Gao, Chongming and Li, Shijun and Zhang, Yuan and Chen, Jiawei and Li, Biao and Lei, Wenqiang and Jiang, Peng and He, Xiangnan},
  url = {https://doi.org/10.1145/3511808.3557624},
  doi = {10.1145/3511808.3557624},
  booktitle = {Proceedings of the 31st ACM International Conference on Information and Knowledge Management},
  series = {CIKM '22},
  location = {Atlanta, GA, USA},
  numpages = {5},
  year = {2022},
  pages = {3953–3957}
}
```

---

## 📌 后续计划

* 模块化数据处理与建模流程
* 支持多反馈目标的多任务学习
* 引入深度模型（如 Transformer）进行序列建模
* 支持线上推理与实验评估

---

欢迎交流与贡献 👋

```

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