AI Product Recommendations

AI product recommendations come from a few classes of model. Collaborative filtering uses purchase and view co-occurrence: shoppers who bought A often buy B, so a shopper looking at A sees B. Content-based recommendation uses product attributes and embeddings: a shopper looking at a navy linen shirt sees other navy linen shirts, plus thematically similar items such as linen trousers. Modern stacks blend both with a re-ranker that adjusts for stock, margin, and freshness.

For on-site placements, the recommender takes a context (homepage, product page, cart, post-purchase) and returns a ranked list of products. For chat-driven recommendations, the assistant translates a freeform query into a search and uses the same ranker to pick what to show. The output is rendered as a card row, an in-chat carousel, or a single recommendation with a reason.

For example, a homepage carousel shows different products to a returning shopper from Bangalore than to a new visitor from Delhi, based on each one's session history. A second example: a shopper viewing a yoga mat sees a strap, a cleaning spray, and a carrying bag as the top recommendations because those three items co-occur in past orders.

Shopify merchants typically have far more SKUs than a homepage can fit, and far more product pages than a shopper will see. AI recommendations turn the catalog into a personalised store, where the few products visible to each shopper are the ones most likely to convert. That can be the difference between a single-item order and a basket order.

The limit is data. Recommenders need event data (views, adds, purchases) to learn. A new store with a thin catalog and no traffic gets diminishing returns from a heavy recommender; a curated rule-based set can perform as well. Shop Me, for example, uses chat context as an additional signal: a recommendation made inside a conversation can use what the shopper just said, which is information no homepage carousel can see.