# Twitter推荐

> twitter比较关注social graph的挖掘

## 1. requirements

**use case & product**

* homepage or related item recommendation
* user: follow
* item: text, image, video
* engagement: click, like, comment, share

**objective**

* increase the engagement

**constraint**

* scale of user and item
* latency

## 2. ML task & pipeline

召回、精排、规则多样性重排、混排

![](/files/bOjqWv8kS6Jn1u36sOjn)

* Fetch the best Tweets from different recommendation sources in a process called candidate sourcing.
* Rank each Tweet using a machine learning model.
* Apply heuristics and filters, such as filtering out Tweets from users you’ve blocked, NSFW content, and Tweets you’ve already seen.

## 3. data collection

* user
  * demographics
* item
  * text
* engagement
  * impression, engagement
* context
  * device
  * time
* label

## 4. feature

* dense
* sparse

## 5. model

### 5.1 retrieval

* In-Network召回
* Out-of-Network 召回

### 5.2 ranking

* MaskNet

### 5.3 reranking

* 过滤已屏蔽用户的推文、NSFW内容和已看过的推文

## 6. evaluation

**offline**

* recall\@k, hit\_rate

**online**

* ctr

## 7. deploy & serving

* batch service or online service
* A/B testing

## 8. monitoring & maintenance

## 9. 优化与问答

## reference

* <https://github.com/twitter/the-algorithm>
* <https://github.com/twitter/the-algorithm-ml>
* [Twitter's Recommendation Algorithm](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm)
* [MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask](https://arxiv.org/pdf/2102.07619.pdf)
* [Twitter是怎么做推荐的？马斯克开源代码梳理](https://zhuanlan.zhihu.com/p/618667508)
* [微博推荐实时大模型的技术演进](https://mp.weixin.qq.com/s/wRi0YJLpru5M1My0H2Ww0w)


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