AI product recommendations
In short
Product suggestions selected using AI models and available signals, such as product attributes, customer interactions, or a stated shopping need.
AI product recommendations select items using a model and available information. Inputs can include product attributes, recorded interactions, or the context of a shopper’s request.
Not every recommendation requires a generative chatbot. Machine learning can power familiar store features such as related items, personalized selections, or products often bought together.
A practical example
Imagine a retailer suggesting accessories after a visitor views a hiking backpack. A recommendation system uses relevant catalog and interaction data to select candidate items.
The retailer still needs rules that prevent unsuitable suggestions, such as accessories incompatible with that backpack. It also checks stock before presenting an offer. This illustrative example separates a model’s relevance prediction from the business rules needed to make the result useful.
A prediction is not a purchase guarantee
Recommendation scores describe the model’s output under its own methodology. They do not automatically represent a probability of purchase or a comparable score across different systems.
Evaluate the resulting customer experience and business outcomes. Check whether recommendations help customers find suitable items, rather than judging only clicks. New products, sparse interaction data, and changing inventory can affect results. Preserve accurate variant information and review any generated explanation for claims the underlying data does not support.