Retuning Amazon's Recommendation Engine to Turn More Browsing into Purchases

Amazon shows product recommendations throughout its shopping experience. As the product manager for recommendations, how would you improve the recommendation algorithm so that it turns more shoppers into buyers? Explain what you would optimize for, what you would change, and how you would show that the change worked. ```hint Decide what counts as a conversion A model can raise clicks, or even attributed purchases, without adding value: it can surface items people would have bought anyway, or items that are later returned. Settle what a recommendation should get credit for before deciding what to change in the model. ``` ### Clarifying Questions - Which surfaces are in scope: the home page, item-to-item carousels on product pages, add-on suggestions in the cart, or recommendation emails? - Should conversion mean purchases that follow a recommendation click, purchases per visit, or the extra purchases that recommendations actually cause? - Are sponsored products placed in recommendation slots in scope, or only organic recommendations? - Is there a priority segment, such as new shoppers with little history, or a category where conversion is weak? ### What a Strong Answer Covers - A precise conversion goal that accounts for incrementality, returns and long-term customer value, with guardrails - A diagnosis of where today's recommendations fail, by surface, shopper segment and failure type (already owned, unavailable, repetitive, wrong intent) - Concrete algorithm and product levers: the ranking objective, eligibility filters, session context, cold start, diversity and explanations - An evaluation path from offline metrics to online experiments that measure incremental purchases rather than attributed clicks - Risks such as feedback loops, popularity bias, privacy in sensitive categories and the effect of sponsored placements on trust ### Follow-up Questions - Your new model raises click-through on recommendations, but total purchases stay flat. What would you investigate? - What would you recommend to a shopper on their first visit, with no history at all? - Leadership wants more sponsored products in recommendation slots. How do you decide how many to allow?