What data do recommendation algorithms use to personalize suggestions?

The Range of Signals Behind a Recommendation

Recommendation algorithms rarely rely on a single type of data. Instead, they combine multiple layers of information collected as a shopper interacts with a site or app. This typically starts with behavioral data — which pages a shopper views, how long they spend on a product, what they add to or remove from a cart, and what they search for. These signals are considered strong indicators of current interest because they reflect real-time actions rather than static profile information.

Layered on top of behavioral signals, algorithms also use structured product data — category, price, brand, and descriptive attributes — to compare items and find similar ones a shopper hasn’t yet seen.

Account, Purchase, and Loyalty Data

For shoppers who are logged in or enrolled in a loyalty program, retailers can draw on a more complete purchase history and link behavior across devices and visits, producing more consistent personalization over time. This might include past order categories, price ranges typically purchased, and how frequently a shopper returns to buy certain kinds of products. Loyalty program data can also incorporate preferences a shopper has explicitly provided, such as favorite categories or sizes, adding a layer of stated preference on top of inferred behavioral signals.

Anonymous or guest shoppers are still personalized to some degree using session-based data and browser identifiers, though this tends to produce less consistent results across separate visits compared to logged-in accounts.

Search Queries, Filters, and Broader Trends

Search terms and filters a shopper applies — such as narrowing results by size, color, or price range — provide some of the clearest explicit signals of intent, since they represent a shopper directly stating what they’re looking for rather than an algorithm inferring it indirectly. Recommendation systems also incorporate broader, non-personal signals like overall product popularity, seasonal trends, and inventory availability, which help fill gaps for shoppers with limited individual history and keep suggestions aligned with what’s currently in demand.

Because data practices differ by retailer and are shaped by applicable privacy laws, the exact scope of data used, and how long it’s retained, is generally described in a retailer’s privacy policy rather than being uniform across the industry.

Bottom Line

Recommendation algorithms typically combine behavioral data, purchase and account history, search intent, and broader product or trend signals to personalize suggestions. The specific mix varies by retailer, and shoppers generally have some ability to review or influence what data is used through account settings or privacy disclosures.