Product recommendation engine
Quick definition
A product recommendation engine is software that suggests the products a shopper is most likely to want. It relies on their behaviour, product attributes, what similar shoppers bought or what they ask for in a conversation.
On this page
The main approaches
Collaborative filtering recommends what people with similar behaviour bought or viewed. Amazon's item-to-item collaborative filtering, described by Linden, Smith and York in 2003, made the “customers who bought this also bought” pattern famous. Content-based filtering recommends products whose attributes resemble those the shopper liked. Rules and merchandising — best-sellers, margins, new arrivals — are layered on top in most stores.
Each approach has a known weakness: collaborative filtering needs a lot of behaviour data and struggles with new products and new visitors, while content-based filtering needs clean, consistent product attributes.
Conversational recommendations
A newer approach lets shoppers state what they need in words: “a gift for a 10-year-old who likes science, under 40 euros”, “a vacuum for a flat with pets”. An AI assistant interprets the request, searches the catalog by meaning and by structured filters — price, stock, size, brand — and explains why each suggestion fits.
This works well for first-time visitors with no history and for considered purchases where shoppers have questions. It depends heavily on product data quality: if sizes, materials or compatibility are missing from the catalog, the assistant cannot use them.
Pitfalls
Recommending out-of-stock products, products that ignore a stated constraint, or the same best-seller to everyone erodes trust quickly. Prices shown in a recommendation must match the live catalog. And recommendations should be judged on the customer's outcome — did they find what they needed? — not only on clicks.
Product recommendations in AI SmartTalk
AI SmartTalk indexes product catalogs from Shopify, PrestaShop, WooCommerce and Joomla, as well as products imported through its API. The assistant searches them by meaning and with filters on price range, stock, brand, category and variant attributes, can exclude terms the shopper rules out, and displays the results as product cards with stock and discount information in the chat.
Frequently asked questions
What is the difference between collaborative and content-based filtering?
Collaborative filtering uses the behaviour of other shoppers (people who bought X also bought Y). Content-based filtering uses the products' own attributes (this jacket is similar to that one).
Do I need a lot of data for product recommendations?
Behaviour-based recommendations do. Conversational and attribute-based recommendations mainly need a well-described catalog, which makes them usable by smaller stores.
Can an AI chatbot recommend products?
Yes, when it is connected to the live catalog. It should filter on real data such as price and stock, rather than rely on the language model's memory of products.