This is the kind of question where you can ramble forever and still feel like you said nothing useful.
Start by acknowledging the limitations of rule-based systems, then propose a hybrid approach that combines collaborative filtering, content-based filtering, and contextual bandits. Emphasize the importance of defining clear business metrics and setting up A/B tests to validate improvements. Finally, discuss how to handle cold-start and scalability challenges.
Pro tip: Frame your answer around measurable business impact—tie each improvement to metrics like click-through rate, conversion, or retention, and mention how you'd validate with A/B tests. This shows you think like a product-minded engineer.
Explain that rule-based systems are static, don't personalize beyond simple attributes, and can't adapt to user behavior over time. This sets the stage for why improvements are needed.
Combine collaborative filtering (user-user or item-item) to capture latent preferences, content-based filtering to use event attributes, and contextual bandits to balance exploration and exploitation.
Use session data, location, time, device, and past interactions to dynamically adjust recommendations. Mention feature stores and online learning for low-latency updates.
Choose offline metrics (precision@k, recall, NDCG) and online metrics (CTR, conversion, revenue). Design A/B tests with proper control and treatment groups, and consider multi-armed bandit for continuous optimization.
Discuss fallback strategies for new users/events (e.g., popularity, content similarity) and system design for scale (e.g., precomputed recommendations, caching, distributed processing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.