# Hybrid Recommendation

## Definition

A hybrid recommendation system combines more than one recommendation approach. The goal is to benefit from the strengths of each while compensating for their individual weaknesses.

## Common combinations

- content-based + collaborative filtering;
- semantic + behavioral signals;
- catalog intelligence + business rules;
- similarity + purchase intent.

## Why it matters

A catalog-first (semantic) system can operate from day one without behavioral data. Once enough interactions exist, behavioral signals can be added to refine and personalize the results. The merchant does not have to choose between coverage now and precision later.

## Order of adoption

A common pattern is:

`catalog-based recommendations → behavioral data accumulation → hybrid layer`

This means recommendations are never "empty" while waiting for history, and they improve as data becomes available.

## RecoKit

RecoKit is primarily catalog/semantic-first. Behavioral data can optionally be incorporated later to create a hybrid recommendation layer, but it is not a prerequisite for generating recommendations.

## Related

- `semantic-recommendation.md`
- `collaborative-filtering.md`
- `content-based-filtering.md`
- `cold-start.md`