Start by clarifying the product goals and constraints (e.g., scale, latency, freshness, personalization vs. trending balance). Then outline a two-stage recommendation architecture (candidate generation + ranking) and a separate trending pipeline that computes real-time aggregates. Finally, discuss trade-offs and how you would serve both at low latency.
Pro tip: Emphasize the separation of concerns: use batch processing for heavy personalization models and stream processing for trending, and cache aggressively at the edge. Also, mention how you would handle cold-start and diversity to avoid filter bubbles.
Ask about scale (users, articles, QPS), latency targets, freshness needs, and business goals (e.g., engagement vs. diversity). This shows you think before designing.
List user features (history, clicks, dwell time, embeddings) and article features (content embeddings, categories, freshness). Discuss how to compute and store them (e.g., feature store, embeddings in vector DB).
Propose a two-stage system: candidate generation (e.g., ANN from user/article embeddings, collaborative filtering) and ranking (e.g., DNN with features). Mention offline training and online serving with low latency.
Describe a streaming pipeline (e.g., Kafka + Flink) that aggregates interactions in sliding windows, computes trending scores (e.g., decayed counts), and stores in a low-latency store (e.g., Redis). Serve via API with caching.
Discuss trade-offs: batch vs. stream, model complexity vs. latency, personalization vs. trending. Mention offline metrics (AUC, NDCG) and online A/B testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.