Ref - Real-time Short Video Recommendation on Mobile Devices
- Generally models the ranking problem as a regression (e.g., predict user’s rating of a video)
- Classification (e.g., predict whether the user will like a video) task
- Pair-wise ranking uses pair-wise loss functions to learn the semantic distance of a pair of items. Distance between embeddings
- model personalization by combining local data set of each user and similar samples retrieved from cloud to train ranking model on device
- Client maintains a watched video list, and all the features and user feedback of each video in the list will be collected and stored. Every time a video is consumed, it will be appended to the list, so we can extract real-time signals from this list with almost no latency
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User Features
- Which accounts you follow
- Creators you’ve hidden
- Comments you’ve posted
- Videos you’ve liked or shared on the app
- Videos you’ve added to your favorites
- Videos you’ve marked as “Not Interested”
- Videos you’ve reported as inappropriate
- Longer videos you watch all the way to the end (aka video completion rate)
- Content you create on your own account
- Interests you’ve expressed by interacting with organic content and ads
Signals from content
- Captions
- Sounds
- Hashtags*
- Effects
- Trending topics
Info collected from device
TikTok’s data processing practices
In total, to reach a FYP stream that recommended one questionable video
out of every four videos, it took:
- An estimated 4 hours and 41 min, Viewing 650 videos in total (41 in the Search stream, 609 in the FYP stream)
- Making 200 likes ظ Liking 25 videos in the search stream (For clarity, these are excluded from the harms tally)
- Liking 175 videos in the FYP stream (of which 146 were questionable videos and 29 were borderline)
- Making four searches for problematic hashtags
- Swiping to ‘skip’ 352 videos before they were finished (an indication to the algorithm that you are not interested in this content).
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