My first instinct was to jump straight into algorithms and collaborative filtering, which was probably the wrong move.
Start by clarifying the goal and scope of the feature, then segment users and use cases to define success metrics. Structure your answer around a user-centric design process: problem definition, ideation, prioritization, and measurement, while tying recommendations to business impact.
Pro tip: Emphasize how you would balance relevance with diversity in recommendations to avoid filter bubbles, and discuss how you'd handle cold-start and data sparsity—showing you understand real-world ML product challenges.
Ask clarifying questions to understand the feature's purpose, target users, and constraints. Define what 'similar' means (e.g., based on attributes, behavior, or content) and the primary success metric (e.g., CTR, conversion, revenue).
Identify key user segments (e.g., new vs. returning, browsing vs. searching) and their needs. Map out use cases where recommendations add value, such as product detail pages, cart, or post-purchase.
Brainstorm potential recommendation approaches (collaborative filtering, content-based, hybrid) and prioritize based on impact, feasibility, and alignment with goals. Consider trade-offs like personalization vs. privacy.
Establish success metrics (e.g., CTR, conversion rate, AOV) and guardrail metrics (e.g., diversity, latency). Plan A/B tests to validate the feature and iterate based on data.
Anticipate challenges like cold-start, scalability, and bias. Outline a roadmap for MVP, learnings, and future enhancements, including feedback loops and model retraining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.