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

• 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

• 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