Recommendation engines grounded in what your users do

There is no universal threshold; it depends on catalogue size and how many interactions each item gets. With a small catalogue or few repeat users, rules and popularity baselines often perform as well as a model. Collaborative filtering needs many users interacting with overlapping items. We run a simple offline comparison on your events and recommend what the data supports, which may be better tracking first.

That is the cold-start problem. New items are placed using their attributes and descriptions: text and image embeddings find similar products that already have history. A share of slots is reserved for exploration so new items collect data, and merchandisers can boost launches. New users see popular, location-relevant items until their own behaviour builds up over a few sessions.

Offline, we check recall and ranking quality on held-out history, which catches weak models early. The real test is online: A/B experiments comparing the new version with the current one on click-through, add-to-cart, conversion or course completion, with a holdout group on the baseline. Tests cover weekday and weekend patterns, and results are read with festive seasonality in mind.

Often, yes. Managed recommendation services from the major cloud providers, or the features built into your e-commerce platform, are a sensible first step for standard product suggestions. A custom build makes more sense when you need Indian-market constraints such as pincode serviceability, COD rules or multi-seller catalogues, want full control over ranking, or serve several apps. We will tell you which applies.