What is Personalized product recommendation?

Personalized product recommendation is the practice of selecting and ranking products for an individual shopper based on their behavior (views, clicks, carts, purchases), context (device, location, time), and product signals (attributes, similarity, availability).

The system predicts what the person is most likely to find useful right now and presents those items in placements such as homepages, category pages, product detail pages, the cart, and email.

When done well, recommendations shorten discovery, increase average order value, and surface relevant alternatives when a size or variant is unavailable. They also help new or long-tail items get visibility without manual merchandising.

Trust is critical: recommendations should respect privacy, avoid repeating out-of-stock or irrelevant items, explain why something is shown when possible, and allow easy dismissal.

Teams blend collaborative patterns (“people who bought X also bought Y”) with content understanding from catalog attributes and images. Rules and guardrails keep results on-brand and in stock, and business goals like margin or newness can be incorporated as soft boosts.

Quality of product recommendation is measured with online A/B tests (clicks, add-to-carts, revenue per session) and through regular audits to prevent drift, duplication, or bias.

Example

A multi-brand fashion retailer uses PDP recommendations that combine style similarity with size availability, so shoppers only see items in their size. On the homepage, a lightweight model re-ranks trending products by the shopper’s preferred price band.
The result is higher engagement and more multi-item baskets without aggressive discounting.

‍