BanditFeed is a small version of the ranking loop behind social feeds,
shopping pages, video apps, and search recommendations: score many
candidates, show a few, observe what the user clicks, then update the
next ranking.
What the model sees
Each article becomes features like topic, length, and recency. Real systems use many more signals: text embeddings, follows, dwell time, shares, freshness, and safety rules.
Why rankings change
Every click or skip updates the bandit's belief about your taste. The match score is a normalized explanation of the current batch ranking, not a guaranteed probability.
Explore vs recommend
A recommender has to balance showing what it already thinks you like with testing uncertain items. That exploration is how platforms avoid getting stuck too early.
Where this appears
Similar feedback loops power For You feeds, product recommendations, news ranking, ad selection, and search result personalization.