# 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


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://blog.tsingjyujing.com/ml/recsys/collection.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.