How does a recommendation engine work?

It gathers signals about the customer and the catalogue, fetches a few hundred likely candidates, ranks them for that person, then applies business rules before showing the results.

Most recommendation engines work in stages. First they gather signals: what the customer has browsed, searched, clicked and bought, what they have said they want, the context they are in, and what similar customers chose. Then they retrieve a few hundred likely candidates from a catalogue that can run to millions of items, rank those candidates for the person, and finally apply business rules such as stock, margin, variety and promotions.

The funnel is so well established that Amazon described using the same stages at RecSys 2026 for a different job: choosing which AI model should power each shopping feature. Newer systems add a generation step, where AI writes the recommendation itself, such as a summary, a plan or a basket.