The interviewer said it was pulled from my resume, so I couldn't exactly pretend I'd never thought about this.
Start by clarifying the business goal and constraints, then outline a high-level system architecture that includes data collection, feature engineering, model training, and serving. Focus on how the recommendation system will drive conversions to premium products, and discuss trade-offs between different approaches.
Pro tip: Emphasize the importance of defining clear success metrics (e.g., conversion rate, lift) and designing A/B tests to measure the impact of recommendations. Also, consider cold-start and scalability from the beginning.
Ask questions to understand the business objective, target users, available data, and constraints (e.g., latency, scale). Define what 'conversion to premium' means and how it will be measured.
Outline the main components: data ingestion, feature store, model training, and online serving. Explain how data flows from user interactions to recommendations.
Discuss what data sources to use (user behavior, demographics, product usage) and how to engineer features that predict premium conversion. Mention handling of cold-start users.
Choose a model (e.g., collaborative filtering, content-based, or hybrid) and justify it. Consider using a two-stage approach: candidate generation and ranking. Explain how to optimize for conversion.
Define offline and online evaluation metrics (e.g., AUC, conversion rate). Describe A/B testing framework and how to iterate based on results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.