# E-commerce Cold Start

## Definition

Cold start is the problem of generating useful recommendations when insufficient historical interaction data exists.

## Product cold start

A new product has no or very few clicks, purchases, ratings or co-purchase relationships.

## User cold start

A new or anonymous visitor has little or no behavioral history.

## Store/catalog cold start

A new store or catalog has too few interactions for behavioral recommendation models to learn reliable relationships.

## Traditional response

Common approaches include:

- popular products;
- manually curated rules;
- metadata-based recommendation;
- content-based recommendation;
- contextual signals;
- hybrid models.

One classical item cold-start method transfers behavioral relationships from
nearby established products. For example, a new pump can inherit the types of
accessories repeatedly bought with comparable pumps. This is safer when the
transfer is constrained by directional product-family association rules and by
critical compatibility attributes rather than copying every neighboring
product relationship.

## Semantic response

Semantic recommendation uses the information contained in the product catalog to derive relationships before behavioral history is available.

## RecoKit

RecoKit is positioned around catalog-based recommendation for cold-start
scenarios. Products receiving advanced AI processing use catalog intelligence.
Products outside that AI allowance retain classical coverage through catalog
similarity, observed co-purchases and relationship transfer from nearby warm
products. Automatic transfers are filtered by source-to-target product-family
admission and critical compatibility rules.

## Related

- `semantic-recommendation.md`
- `collaborative-filtering.md`
- `../relationships/recokit-cold-start.md`