How do AI shopping assistants decide what to recommend?

They combine what the customer asks for in the conversation, what the store already knows about them, and the product data, price and stock the assistant can read at that moment.

The assistants Target and Infobip showed at RecSys 2026 start from a goal, such as planning meals for the week or getting everything needed to make pizza. Infobip’s asks up to three questions to fill gaps like budget or dietary needs, then searches the catalogue and returns a grouped basket with prices.

Target’s assistant also draws on the shopper’s purchase history, translated into plain-language preferences such as “likes cooking from scratch” or “affinity for Thai cuisine,” and tells the shopper where those came from. Infobip’s assistant searches a catalogue index kept in sync with price and stock changes, so it does not recommend products that are unavailable.

If an assistant cannot read a product’s attributes or availability, it has very little to recommend that product on.