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

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