You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop…

Question

Options

• AUse the "Other Products You May Like" recommendation type to increase the click-through rate.
• BUse the "Frequently Bought Together" recommendation type to increase the shopping cart size
• CImport your user events and then your product catalog to make sure you have the highest quality
• DBecause it will take time to collect and record product data, use placeholder values for the

• A
  4% (2)
• B
  75% (36)
• C
  15% (7)
• D
  6% (3)

Explanation

The 'Frequently Bought Together' recommendation type is specifically designed to increase average order value by surfacing products that are commonly purchased together, directly growing shopping cart size and thus revenue. This aligns with the goal of maximizing revenue rather than just engagement metrics. 'Other Products You May Like' (Option A) optimizes for click-through rate, which does not directly correlate with revenue. Option C describes incorrect sequencing - best practice in Recommendations AI is to import the product catalog first and then import user events, not the other way around. Option D is a poor practice because placeholder values corrupt training data quality and lead to degraded recommendation accuracy from the outset.

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