Instruction: Discuss the steps you would take, from data collection to model deployment, to create an effective recommendation system.
Context: This question assesses the candidate's ability to apply machine learning techniques to solve real-world problems in the e-commerce sector, focusing on system design and practical application.
I would start with the business objective because recommendations can optimize very different things: conversion, revenue, discovery, repeat purchase, or long-term customer value. That choice affects everything from label design to ranking logic.
For a new platform, cold start is the first real problem, so I would not begin with a purely collaborative approach. I would use content and catalog features for initial candidate generation, layer in popularity and business rules where needed, and then add behavioral signals as interaction data accumulates. Over time I would separate the system into candidate generation and ranking so it can scale, personalize, and support different surfaces such as home page, PDP, cart, and email.
I would also evaluate beyond CTR. Recommendations should be judged on quality, diversity, coverage, and downstream business value, otherwise the system can become a narrow click optimizer that hurts the broader shopping experience.
A weak answer says "use collaborative filtering" without dealing with cold start, surface-specific objectives, or how the system will be measured beyond clicks.
medium
hard
hard
hard