https://github.com/yj8023xx/recsys-tutorial
推荐系统入门教程，包含基础知识和相应的运行实例
https://github.com/yj8023xx/recsys-tutorial
pytorch recommender-system tutorial
Last synced: 5 months ago
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推荐系统入门教程，包含基础知识和相应的运行实例
- Host: GitHub
- URL: https://github.com/yj8023xx/recsys-tutorial
- Owner: yj8023xx
- Created: 2023-01-02T11:51:49.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-09T12:32:35.000Z (about 2 years ago)
- Last Synced: 2025-04-11T11:53:57.767Z (10 months ago)
- Topics: pytorch, recommender-system, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 6.76 MB
- Stars: 10
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RecSys-tutorial
本项目收集整理了推荐系统的基础知识，常用的推荐系统模型代码示例，适合初学者，项目还在不断完善当中！
## 内容导航
### 基础知识
- [推荐系统](https://github.com/yj8023xx/recsys-tutorial/blob/main/basics/recommender_system.ipynb)
- [Embedding](https://github.com/yj8023xx/recsys-tutorial/blob/main/basics/embedding.ipynb)
- [相似度](https://github.com/yj8023xx/recsys-tutorial/blob/main/basics/similarity.ipynb)
- [指标](https://github.com/yj8023xx/recsys-tutorial/blob/main/basics/metric.ipynb)
- [评估](https://github.com/yj8023xx/recsys-tutorial/blob/main/basics/evaluate.ipynb)
### 传统模型
| 序号 | 会议 | 模型 | 论文链接 |
| :--: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| 1 | - | [CF](https://github.com/yj8023xx/recsys-tutorial/blob/main/basic_model/01_neighborhood-based.ipynb) | - |
| 2 | - | [LFM](https://github.com/yj8023xx/recsys-tutorial/blob/main/basic_model/02_latent_factor_model.ipynb) | - |
| 3 | - | [FM](https://github.com/yj8023xx/recsys-tutorial/blob/main/basic_model/03_factorization_machine%20.ipynb) | [Factorization Machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| 4 | `ADKDD'14` | [GBDT_LR](https://github.com/yj8023xx/recsys-tutorial/blob/main/basic_model/04_gbdt_lr.ipynb) | [Practical Lessons from Predicting Clicks on Ads at Facebook]() 🚩**Facebook** |
### 神经网络模型
| 序号 | 会议 | 模型 | 论文链接 |
| :--: | :--------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| 1 | `CIKM'13` | [DSSM](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/01_dssm.ipynb) | [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data ](https://posenhuang.github.io/papers/cikm2013_DSSM_fullversion.pdf)🚩**Microsoft** |
| 2 | `DLRS'16` | [Wide&Deep](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/02_wide%26deep.ipynb) | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) 🚩**Google** |
| 3 | `ICDM'16` | [PNN](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/03_pnn.ipynb) | [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) |
| 4 | `KDD'16` | [DeepCrossing](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/04_deepcrossing.ipynb) | [Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) 🚩**Microsoft** |
| 5 | `WWW'17` | [NCF](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/05_ncf.ipynb) | [Neural Collaborative Filtering](https://dl.acm.org/doi/10.1145/3038912.3052569) |
| 6 | `IJCAI'17` | [DeepFM](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/06_deepfm.ipynb) | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/abs/1703.04247) 🚩**Huawei** |
| 7 | `SIGIR'17` | [NFM](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/07_nfm.ipynb) | [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/citation.cfm?id=3080777) |
| 8 | `IJCAI'17` | [AFM](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/08_afm.ipynb) | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/0435.pdf) |
| 9 | `ADKDD'17` | [DCN](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/09_dcn.ipynb) | [Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) 🚩**Google** |
| 10 | `KDD'18` | [xDeepFM](https://github.com/yj8023xx/recsys-tutorial/blob/main/nerual_network_model/10_xdeepfm.ipynb) | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf) 🚩**Microsoft** |
## 参考资料
- **[FuxiCTR](https://github.com/xue-pai/FuxiCTR)**
- 《深度学习推荐系统》 王喆
- **[fun-rec](https://github.com/datawhalechina/fun-rec)**