multi objective

Feed ranking is multi-objective ML at massive scale with a harsh serving constraint. The interviewer wants to see whether you optimize a value model toward long-term satisfaction, or fall into the CTR trap that breeds clickbait. Here is the design.

A news feed differs from a generic recommender. Time is a first-class signal: a story that mattered this morning is noise by tonight. The interviewer expects recency decay, an engagement-versus-quality value model, and a plan for filter bubbles, not merely retrieval plus ranking.

With billions of candidate items and only tens of milliseconds to choose the next 10, a feed is a latency problem first. The interview probes the two-stage architecture, how you set the objective when engagement fights integrity, and the biases that silently corrupt your training labels.