⚙️ 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.
By Headlinne Editorial Team · Updated on
A multi-stage pipeline
Modern recommendation systems rarely score every item against every user in one pass—that does not scale. Instead they use a funnel: a fast retrieval stage narrows millions of items to a few hundred candidates, then a heavier ranking stage scores those candidates precisely, and a final assembly stage applies business rules like diversity and freshness.
Headlinne follows this pattern. A candidate pool is fetched from the active, unexpired article set, augmented with semantically similar articles, scored against a behavioral profile, bucketed by relevance, and assembled into a final feed under diversity constraints.
The three stages in Headlinne
Each stage has a distinct job:
- Candidate generation — retrieve a manageable pool (recency-ordered fresh articles plus semantic nearest-neighbors from a taste vector)
- Ranking — score each candidate with a weighted blend of behavioral, semantic, and crowd signals
- Assembly — compose the final feed with diversity caps, freshness filtering, and exploration slots
Signals, not a single number
Headlinne's ranking is a weighted sum of about ten signals whose weights sum to one: granular subtopic affinity carries the most weight, followed by geographic relevance and a semantic similarity score, then broad topic, entity, publisher, recency, reading-completion, trending, and collaborative signals.
Keeping signals separate and weighted makes the system interpretable and tunable. You can see which component drove a recommendation and adjust weights without rewriting the whole model.
Designed to degrade gracefully
A production engine must keep working when parts are missing. Headlinne's cross-user signals (trending, collaborative filtering) read from precomputed tables, and every optional read falls back to a neutral value if the data is unavailable.
This means the engine produces a sensible feed even for a brand-new user with no history, or before a background job has populated a table—no single missing input breaks the feed.
Key takeaways
- ✓Recommendation engines use a funnel: candidate generation, ranking, then assembly.
- ✓Headlinne ranks with a weighted blend of ~10 behavioral, semantic, and crowd signals.
- ✓Optional signals degrade to neutral values so the feed never breaks.
Frequently asked questions
Why not score every article for every user?
It does not scale. A retrieval stage first narrows the corpus to a few hundred candidates, so the expensive ranking stage only runs on a small, promising set.
What makes a recommendation "explainable"?
Because Headlinne ranks by a transparent weighted sum of named signals, each recommendation can be traced to the components that drove it, rather than an opaque black-box score.
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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.
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.
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