This question is basically six questions stitched together and the interviewer will pull on whichever thread they want, so pacing yourself is genuinely hard.
Start by clarifying the product objectives and success metrics, then walk through the full pipeline: data collection, candidate generation, ranking, and online evaluation. Emphasize trade-offs between relevance, diversity, and freshness, and how guardrails protect user experience and platform health.
Pro tip: Frame the system around LinkedIn's professional context: short videos should drive meaningful engagement (e.g., skill-building, networking) not just watch time. Show you understand that optimizing for long-term value requires careful label design and guardrails against clickbait.
Clarify business goals (e.g., increase meaningful engagement, retention) and translate them into measurable online metrics (e.g., watch time, likes, shares, comments) and offline proxies. Consider counter-metrics like report rate or skip rate.
Identify data sources (user interactions, video metadata, social graph) and define labels for training (e.g., binary engagement, watch percentage, dwell time). Address biases like position bias and feedback loops.
Outline a two-stage architecture: candidate generation (e.g., collaborative filtering, content-based) and ranking (e.g., deep learning models). Discuss feature engineering, model choices, and trade-offs between relevance, diversity, and freshness.
Describe A/B testing methodology, metric selection, and how to measure long-term effects. Include interleaving or bandit approaches for faster iteration.
Define guardrail metrics (e.g., user reports, unfollows, session abandonment) and monitoring systems to detect degradation. Discuss fallback strategies and ethical considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.