⚙️ 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.
By Headlinne Editorial Team · Updated on
The problem of no history
Recommendation engines learn from behavior—but a new user has none. This is the "cold start" problem: on day one, the system knows nothing about what you like, yet still has to produce a useful first feed. A poor first experience causes users to leave before the engine can learn anything.
Cold start also applies to new items (a just-published article no one has engaged with) and to systems that have few users overall.
Starting with priors
One solution is to gather a little explicit information up front. Headlinne lets new users select topics and regions of interest during onboarding, giving the engine "priors" to rank against before any behavior exists.
These onboarding preferences dominate ranking early, then gradually give way to real behavior as the user swipes—though they never fully decay to zero when explicitly set, so preferences you choose in Settings are always respected.
More exploration when uncertain
When the engine knows little, it should explore more. Headlinne gives cold-start users a more discovery-heavy feed mix than established users, deliberately showing a wider variety of content to learn preferences quickly.
As interaction count crosses a threshold, the user transitions to a mature, behavior-driven mix with a smaller—but never zero—exploration budget.
Neutral defaults everywhere
Until a signal exists, the engine assumes neutrality:
- Unknown topics and entities get middle-of-the-road affinity scores
- Semantic scoring stays flat until a taste vector is built
- Crowd signals fall back to neutral values
- The result is a reasonable feed that improves rapidly with use
Key takeaways
- ✓Cold start is the challenge of recommending to users (or items) with no history.
- ✓Headlinne uses onboarding priors and a discovery-heavy mix for new users.
- ✓Priors give way to behavior over time but never fully vanish when explicitly set.
Frequently asked questions
How long until my feed feels personalized?
Headlinne noticeably shifts from a balanced cold-start mix to a behavior-driven feed after around 20 interactions, and keeps refining from there with continued use.
Does onboarding topic selection matter long-term?
Yes. Explicit interests never decay to zero influence, so topics you choose in Settings continue to shape your feed even after heavy use.
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