Twitter推荐

twitter比较关注social graph的挖掘

1. requirements

2. pipeline

召回、精排、规则多样性重排、混排

• 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

4. feature

5. model

retrieval

• In-Network召回

• Out-of-Network 召回

ranking

• MaskNet

reranking

• 过滤已屏蔽用户的推文、NSFW内容和已看过的推文

6. evaluation

reference

• https://github.com/twitter/the-algorithm

• https://github.com/twitter/the-algorithm-ml

• Twitter's Recommendation Algorithm

• MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

• Twitter是怎么做推荐的？马斯克开源代码梳理

• 微博推荐实时大模型的技术演进