This is a big one and I underestimated how many sub-parts they actually wanted me to cover.
Start by clarifying the business objective and constraints, then propose a multi-objective ranking framework that balances user experience, advertiser value, and platform revenue. Walk through the full ML lifecycle—from problem formulation to evaluation—while emphasizing trade-offs and mitigation strategies.
Pro tip: Anchor your answer in a north-star metric like long-term user value, and explicitly discuss how you'd measure incremental impact via A/B tests or holdouts to avoid confounding from ad selection bias.
Clarify the primary goal (e.g., maximize conversions without harming user retention) and constraints (e.g., advertiser fairness, latency, policy). Translate into a multi-objective optimization problem.
Identify features from user, ad, context, and advertiser dimensions. Define labels such as conversion, click, and user satisfaction proxies (e.g., dwell time, hide rate).
Propose a multi-task or multi-objective model (e.g., MMoE) to predict multiple outcomes, then combine predictions into a final ranking score using a utility function that weights business goals.
Incorporate advertiser-specific objectives (e.g., ROAS, CPA) via constraints or personalized weights. Consider fairness and budget pacing mechanisms.
Design offline metrics (e.g., AUC, calibration) and online A/B tests with guardrail metrics. Identify risks like feedback loops, ad fatigue, and user experience degradation, and propose mitigations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.