Start by framing the problem as a real-time recommendation system with a feedback loop, then walk through each component (data, features, models, serving, evaluation) in a logical order. Emphasize trade-offs between latency, accuracy, and scalability, and tie your choices back to business metrics like CTR and revenue. Conclude by discussing how you'd iterate using A/B tests and handle cold-start users.
Pro tip: Show that you understand the end-to-end system isn't just about the model—it's about the data pipeline, serving infrastructure, and experimentation culture. Mention concrete examples of how you'd monitor and mitigate issues like feedback loops or position bias.
Describe the data sources (user interactions, ad content, context) and how you'd process them into features for training and serving. Highlight the need for consistent feature computation offline and online.
Discuss candidate models (e.g., logistic regression, GBDT, deep learning) and why you'd choose one based on latency, interpretability, and performance. Explain how you'd train and validate the model.
Outline the serving architecture: candidate generation, feature fetching, model inference, and ranking. Address latency constraints and fallback strategies.
Explain how you'd collect feedback (clicks, conversions) and use it to update models, either in batch or online. Discuss challenges like feedback loops and how to mitigate them.
Describe how you'd design A/B tests to measure the impact of model changes, including offline metrics (AUC, log loss) and online metrics (CTR, revenue). Discuss cold-start handling and guardrail metrics.
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