Design recommendations objective balancing growth and monetization
Overview
This question evaluates a data scientist's competency in designing multi-objective recommender systems that balance user growth and creator monetization, covering ranking objective formulation, per-impression value estimation, constraint enforcement (latency, diversity, fairness/exposure), offline-to-online metric validation, cold-start and exploration strategies, and interference/attribution challenges within the Machine Learning domain. It is commonly asked to assess the ability to reason about trade-offs between business and user metrics, to define measurable objectives and validation plans, and to demonstrate both conceptual understanding and practical application of system design and evaluation.
Design a Multi-Objective Recommender for Long-Form Content
You are designing the ranking objective and measurement plan for a long-form content recommender that must balance user growth and creator monetization. Assume a standard two-stage system (recall → rank) and a feed or slate of K items.
Tasks
- Objective and Constraints
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Propose an explicit optimization objective that balances:
- User growth/engagement (e.g., expected session retention, dwell time, next-day return).
- Monetization (e.g., purchase probability, creator revenue/platform margin).
- Define how you estimate per-impression value for each candidate item and position.
- Describe how you will enforce constraints such as latency, diversity (intra-list similarity), and fairness/exposure guarantees for new creators.
- Offline Metrics and Correlation to Online
- Define offline metrics (e.g., NDCG weighted by expected revenue/utility, probability calibration for purchase/monetization, coverage/diversity metrics).
- Explain how you will validate that offline metrics predict online A/B outcomes; include a plan to mitigate training–serving skew.
- Cold Start and Safe Exploration
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Explain how you will handle cold-start for new users and new creators:
- Features, priors, and modeling approaches.
- Exploration strategy (e.g., contextual bandits) and how to throttle exploration to avoid revenue cliff risk.
- Interference and Attribution
- Describe how you will manage interference between organic ranking and monetization ads/sponsored surfaces.
- Explain how you would attribute incremental revenue to the recommender when ad delivery and content ranking interact.
Solution
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