I jumped straight into the algorithm side of things and the interviewer kind of steered me back toward the product framing.
Start by clarifying the business objective and user context, then outline a recommendation system design that balances multiple signals (e.g., purchase history, browsing, similarity) and addresses cold start. Finally, define a metrics framework that includes offline evaluation, online A/B testing, and long-term business impact.
Pro tip: Emphasize the importance of measuring both immediate engagement metrics and long-term customer satisfaction, as Amazon prioritizes customer obsession over short-term gains. Also, mention the need to consider the trade-offs between different recommendation strategies (e.g., personalization vs. diversification).
Ask clarifying questions to understand the primary objective (e.g., increase sales, improve customer experience) and the target user segments. This ensures the design aligns with Amazon's customer-centric approach.
Describe the key components: data sources (user behavior, product catalog), algorithms (collaborative filtering, content-based, hybrid), and infrastructure (real-time vs. batch processing). Mention how to handle cold start and scalability.
Propose a mix of offline metrics (precision, recall, NDCG) and online metrics (CTR, conversion rate, revenue per user). Include business metrics (AOV, customer lifetime value) and guardrail metrics (page load time, diversity).
Explain how to run A/B tests to validate the system, measure impact, and iterate. Highlight the importance of statistical significance and long-term holdout groups.
Discuss potential trade-offs (e.g., relevance vs. diversity, short-term vs. long-term) and risks (e.g., filter bubbles, privacy concerns). Show awareness of Amazon's scale and customer trust.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.