> For the complete documentation index, see [llms.txt](https://blog.tsingjyujing.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://blog.tsingjyujing.com/ml/recsys/collection.md).

# 资料收集

## 博客

* [见微知著，你真的搞懂Google的Wide\&Deep模型了吗？](https://zhuanlan.zhihu.com/p/142958834)
* [知识蒸馏在推荐系统的应用](https://zhuanlan.zhihu.com/p/143155437)
* [Ctr 预估之 Calibration](https://zhuanlan.zhihu.com/p/142086309)
* [推荐系统中稀疏特征Embedding的优化表示方法](https://zhuanlan.zhihu.com/p/141517705)
* [Learning to Rank学习笔记--ListwiseRank](https://zhuanlan.zhihu.com/p/66514492)
* [推荐系统 embedding 技术实践总结](https://zhuanlan.zhihu.com/p/143763320)

## 开源项目

* [wzhe06/CTRmodel](https://github.com/wzhe06/CTRmodel):Spark上的CTR模型
  * Naive Bayes
  * Logistic Regression
  * Factorization Machine
  * Random Forest
  * Gradient Boosted Decision Tree
  * GBDT + LR
  * Neural Network
  * Inner Product Neural Network (IPNN)
  * Outer Product Neural Network (OPNN)
* [jrzaurin/pytorch-widedeep](https://github.com/jrzaurin/pytorch-widedeep):Pytorch实现的Deep & Wide
* [allegro/allRank](https://github.com/allegro/allRank): Pytorch 实现的 Learning To Rank
* [haowei01/pytorch-examples](https://github.com/haowei01/pytorch-examples): Pytorch的，L2R，CF算法的例子。
* [microsoft/recommenders](https://github.com/microsoft/recommenders): 微软的推荐系统最佳实践


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