Start by clarifying the product context and scale (e.g., Snapchat's short-form video, user base, engagement metrics), then walk through the two-stage architecture: candidate generation (retrieval) and ranking. Emphasize how the stages align through shared objectives, feature consistency, and feedback loops, and discuss trade-offs between relevance, diversity, and latency.
Pro tip: Highlight that candidate generation and ranking must be trained and evaluated with the same business metrics (e.g., watch time, completion rate) to avoid objective mismatch, and mention how you'd use online A/B testing to validate alignment.
Ask about scale (DAU, video inventory), latency constraints, and success metrics (e.g., CTR, watch time, user retention). Define the problem scope and constraints.
Propose multiple retrieval sources (e.g., collaborative filtering, content-based, trending, social graph) to generate a few hundred candidates. Discuss how to balance recall and diversity.
Describe a multi-stage ranking (e.g., lightweight ranker then heavy ranker) using features like user history, video metadata, and context. Explain model choice (e.g., DNN, GBDT) and optimization for business metrics.
Explain how to keep candidate generation and ranking aligned: shared feature store, consistent objective functions, joint training or distillation, and feedback loops from ranking to retrieval.
Discuss offline evaluation (e.g., recall@k, NDCG) and online A/B testing. Mention monitoring for drift, diversity, and fairness, and how to iterate based on results.
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