Two versions of the cold start problem
In recommender systems, cold start exists in two independent forms.
User cold start — a new visitor with no interaction history. Collaborative filtering has nothing
to work with: there is no one to compare them against. The system knows neither their interests, nor
their price range, nor their brand preferences.
Product cold start — a new item in the catalogue with no views, add-to-cart events or purchases.
The algorithms do not know who this product would appeal to. The result is that a new arrival is
invisible in recommendations and gets only the organic traffic that reaches it through the catalogue.
Strategies for solving it
For a new user
| Strategy | How it works | Time to first signal |
|---|---|---|
| Session-based recommendations | Built on views in the current visit | Immediate — from the first click |
| Popularity-based fallback | Bestsellers and trends | No signal, so no personalization |
| Onboarding questions | What are you interested in? | After 2–3 answers |
| Geotargeting | Local preferences by region | On the first visit |
| Identity resolution | Linking to offline history | On sign-in |
For a new product
- Content-based algorithm — uses attributes (category, brand, price, specifications) to find
similar products - Cluster boost — the item is assigned to the nearest cluster and inherits that cluster’s audience
- Merchandising rules — manual promotion of new arrivals to the top of recommendations
- Explorative strategies — epsilon-greedy shows new items in a small share of impressions to
collect signals
The affinity profile as a solution
Instead of waiting for purchase history to accumulate, the system builds a profile of the shopper
from session behaviour: what they viewed, how long they spent on a page, what they added to the cart.
After as few as 5–10 viewed products, the algorithm has a read on the price range, the category of
interest and the brand preferences. That shortens the cold-start period substantially — from several
weeks to a single session.
Important: never leave cold start without a fallback strategy. Empty recommendation blocks, or
placeholders reading products coming soon, are worse than popular products used as filler.