Relevant beats clever
A recommender only earns its keep if it shows people things they actually want. Accuracy on a benchmark means nothing if it doesn't move clicks, baskets, or retention.
Approaches that work
- Collaborative filtering: people like you also liked.
- Content-based: more of what you already engaged with.
- Hybrids that blend both, plus sensible business rules.
- Guardrails against filter bubbles and stale picks.
Measure the business, not the model
We tune recommendations against real outcomes—conversion, order value, retention—and keep testing, because tastes and inventory never stop changing.
