Machine Learning Advanced Pro

Case Study: Long-Term Engagement Recommender

Adapt a feed ranking system to optimize cumulative long-term value instead of immediate clicks

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Model interview answer for redesigning a recommendation feed to optimize cumulative long-term engagement rather than immediate clicks: surrogate rewards built from a learned retention/session-return predictor, delayed-feedback handling in the training pipeline, exploration inside the retrieval-to-re-ranking funnel, and why a short A/B test on CTR can be actively misleading for a long-term-optimizing model.

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Practice questions (5)

Redesign a Click-Dominated Value Formula for Long-Term Value

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Handle Surrogate-Target Immaturity for the Freshest Impressions

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Design Exploration Across the Full Ranking Funnel

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Respond to a Stakeholder Reading a Short A/B Test as Failure

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Diagnose a Stale Surrogate Caused by Insufficient Exploration

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Quizzes (1)

Case Study: Long-Term Engagement Recommender Quiz

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