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Learn about personalized news
94 guides on how Headlinne works, AI-powered features, media bias, journalism, and building healthier news habits.
By Headlinne Editorial Team · Updated on
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Getting Started
Understand what Headlinne is, how it works, and how to get the most from your feed.
8 articles
🗞️News Basics
Start here: what news is, what makes a story newsworthy, and how journalism actually works.
20 articles
📰News & Journalism
Explore how modern news works, why headlines mislead, and what good journalism looks like.
7 articles
🤖Artificial Intelligence
Learn how Headlinne uses AI for summaries, search, recommendations, and bias analysis.
8 articles
⚖️Media Bias
Understand media bias, how it is measured, and how to read news more critically.
7 articles
📱Product Features
Deep dives into swipe-based news, personalized feeds, Dive Deeper, and more.
12 articles
🆚Comparisons
Balanced comparisons between Headlinne and other popular news apps.
14 articles
⚙️Recommendation Engine
Developer-focused deep dives into how Headlinne ranks, retrieves, and personalizes news.
6 articles
💼For Your Industry
How AI-powered news helps professionals in finance, healthcare, law, tech, and more.
6 articles
❓FAQ
Answers to common questions about pricing, privacy, availability, and personalized news.
6 articles
All articles
⚙️ Recommendation Engine
Recommendation Engine Architecture
A technical overview of how Headlinne's recommendation engine turns a stream of articles into a personalized, diverse feed—from candidate generation to ranking to assembly.
⚙️ Recommendation Engine
Candidate Generation Explained
Before ranking anything, a recommender must retrieve a pool of candidates. Learn how Headlinne combines recency-based and semantic retrieval to build its candidate set.
⚙️ Recommendation Engine
Embeddings and Vector Search
Embeddings turn articles and interests into vectors, and vector search finds the closest matches. Learn how Headlinne uses them to power semantic recommendations and search.
⚙️ Recommendation Engine
Cosine Similarity Explained
Cosine similarity measures how alike two vectors are by the angle between them. Learn how it works and why recommendation engines use it to compare interests and articles.
⚙️ Recommendation Engine
The Cold Start Problem
How does a recommender help a brand-new user with no history? Learn what the cold start problem is and how Headlinne handles new users with priors and more exploration.
⚙️ Recommendation Engine
Exploration vs. Exploitation
Every recommender must balance showing what it knows you like against discovering new interests. Learn this core trade-off and how Headlinne uses Thompson sampling to solve it.
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