Start by clarifying the search/ranking use case, scale, and business objectives (e.g., product search on Amazon). Then outline a two-stage architecture: candidate generation (retrieval) and ranking (scoring), and discuss trade-offs in model choice, features, and evaluation. Emphasize how you would iterate and measure success with offline and online metrics.
Pro tip: Anchor your design in Amazon's leadership principles: customer obsession (relevance), ownership (end-to-end metrics), and bias for action (start simple, iterate). Mention how you'd handle cold start and feedback loops to show depth.
Ask questions to understand the use case: what is being searched/ranked (e.g., products, documents), scale (QPS, catalog size), latency constraints, and business metrics (CTR, conversion). Define success criteria and constraints.
Propose a two-stage system: candidate generation (retrieval) to narrow millions of items to hundreds, then ranking to order them. Mention components like query understanding, feature store, and serving infrastructure.
Describe methods: inverted index (BM25) for lexical, embeddings (two-tower) for semantic, and hybrid. Discuss trade-offs: recall vs. latency, and how to handle cold start.
Explain the ranking model: e.g., gradient boosted trees or deep neural networks (DNN) with features like query-item relevance, user history, item popularity. Discuss multi-objective optimization (relevance, revenue) and business rules.
Cover offline metrics (NDCG, MRR) and online A/B testing (CTR, conversion). Discuss feedback loops, position bias, and how to monitor and retrain models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Experimentation in ranking is genuinely tricky because standard A/B tests can have interference effects and position bias makes metrics noisy.
Start by clarifying the goal and metrics, then outline a structured experiment design covering hypothesis, randomization, and statistical analysis. Emphasize online A/B testing with guardrail metrics and discuss how to evaluate results for ranking/search systems.
Pro tip: Highlight the importance of defining a clear, single primary metric and pre-registering the experiment to avoid p-hacking. Also, mention that you would run a power analysis to determine sample size and duration.
Clarify the business goal and formulate a testable hypothesis. Identify the primary metric (e.g., CTR, conversion) and guardrail metrics (e.g., latency, revenue).
Choose the experiment type (e.g., A/B test), define control and treatment groups, and determine randomization unit (e.g., user, session). Ensure proper sampling and avoid biases.
Perform power analysis to calculate required sample size and experiment duration. Consider traffic, effect size, and significance level to ensure valid results.
Launch the experiment, monitor for technical issues, and track guardrail metrics. Ensure data quality and check for sample ratio mismatch (SRM).
Analyze results using statistical tests (e.g., t-test), check for significance and practical impact. Consider segment analysis and decide whether to launch, iterate, or abandon.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.