Feed Curation
Personalize RSS feed articles with adaptive Loop models that learn from user interactions.
Personalize RSS feed articles with adaptive Loop models that learn from user interactions.
Business problem
Feed aggregators surface thousands of articles daily. Static ranking cannot adapt to individual preferences, hurting engagement.
Heimdall solution
Publish interaction and article features from Lake, then deploy a Heimdall Loop model that learns from clicks, dwell time, and feedback in real time.
Business impact
Adaptive ranking improves engagement and time-on-site while reducing manual curation work.
How it works
Catalog events in Lake
Ingest article metadata and user interaction logs to bronze and gold.
Deploy a Loop model
Online learning adjusts rankings as new interactions arrive.
Serve personalized feeds
Call Loop APIs from your app to rank articles per user.
See Heimdall in action
Tell us about your recommendation or feed product
Key benefits
Real-time adaptation
Rankings update with each interaction
Higher engagement
Surface relevant articles automatically
Less manual curation
Loop learns preferences continuously
Production APIs
Integrate ranking into existing feed apps
Governed features
Gold tables define what the loop sees
Classification or regression
Match Loop mode to your ranking task
Related use cases
More scenarios on the use cases hub.
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