How the recommendation feed works in 7 steps:
1️⃣ Raw data (input):
- social graph (who follows whom),
- engagement (likes, retweets, replies, bookmarks),
- user data (clicks, profile, behavior).
2️⃣ Feature Engineering:
- GraphJet — real-time tweet graph
- SimClusters — community grouping ("AI Twitter", "NBA Twitter")
- TwHIN — user↔tweet connection map
- RealGraph — strength of connections
- TweepCred — trust scoring
- Trust & Safety signals
3️⃣ Candidate Sourcing (Home Mixer):
Different mixers (CR Mixer, UTEG, FRS) pull tweets from various pools → more diversity.
4️⃣ Heavy Ranker (ML model):
Neural network predicts what you will like: likes, retweets, replies, reading time.
5️⃣ Filters and heuristics:
- social proof
- author diversity
- spam/NSFW/mute blocks
- content balance
- protection against "filter bubble"
6️⃣ Mix:
Promoted tweets + "who to follow" recommendations → into the feed.
7️⃣ What this means for you:
- choose a niche
- write valuable posts
- respond meaningfully in your topic
→ grow your audience and find people/ideas for business.
GitHub
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🤖 Data Science, ML & Big Data with @DataXplore