🤖 Artificial Intelligence
How AI Recommendations Work
Headlinne's recommendation engine learns from your swipes, reads, and searches to build a feed that gets more relevant over time.
By Headlinne Editorial Team · Updated on
Behavioral signals
Every interaction is a signal. Right swipes boost topic and source affinity. Left swipes reduce them. Full reads indicate deep interest. Search queries reveal latent interests not yet seen in your feed.
Collaborative filtering
Headlinne also uses patterns from users with similar taste profiles. If readers who like the same articles as you also engage with certain other stories, those stories get a relevance boost in your feed.
Exploration vs. exploitation
A pure exploitation model would show only what you already like—creating an echo chamber. Headlinne reserves exploration slots for articles outside your typical pattern, introducing new topics and perspectives.
Profile management
Your recommendation profile updates continuously. You can reset it in Settings to start fresh—useful if your interests have changed or you want to break out of a pattern.
Key takeaways
- ✓Swipes, reads, and searches all train your profile.
- ✓Collaborative filtering surfaces stories liked by similar users.
- ✓Exploration slots prevent echo-chamber feeds.
Frequently asked questions
How fast does the feed adapt?
Meaningful changes appear within a session. Significant personalization develops over several days of use.
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Why Personalized News Isn't an Echo Chamber
Personalization and echo chambers are often conflated. Headlinne personalizes topics and relevance—not political ideology.
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