This is one of those questions that looks manageable until they start asking why at every single step.
Start by outlining the full pipeline from data ingestion to serving, then zoom into each stage (candidate generation, ranking, re-ranking) and explain how personalization is introduced incrementally. Emphasize trade-offs, evaluation metrics, and how the system evolves from popularity-based to personalized using feedback loops and A/B testing.
Pro tip: Frame your answer around LinkedIn's specific context—mention how signals like connections, skills, and job history feed into the system, and highlight the importance of online metrics (CTR, dwell time) over offline ones for iterative improvement.
Describe the types of data (user profiles, interactions, content metadata) and how they are collected and stored (batch vs. streaming). Mention the need for data freshness and scalability.
Explain how raw data is transformed into features (user, item, context) and stored in a feature store for low-latency access. Highlight techniques like embeddings, normalization, and handling categorical variables.
Outline methods to narrow down millions of items to hundreds (e.g., collaborative filtering, content-based, or two-tower models). Discuss how personalization starts here with user-specific candidates.
Describe the ranking model (e.g., deep neural networks) that scores candidates, and re-ranking steps that apply business rules, diversity, and freshness. Mention how personalization deepens with user features.
Cover offline metrics (precision, recall, NDCG) and online A/B testing (CTR, engagement). Explain how feedback loops and experimentation drive the evolution from popularity to personalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.