candidate generation

PYMK is graph recommendation at billion-node scale. The interviewer is screening for one instinct: do you generate candidates from the social graph, or naively try to score all pairs? Here is the design that survives the follow-ups.

Feed ranking is multi-objective ML at massive scale with a brutal 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 is not a generic recommender. The clock is a first-class signal: a story that mattered this morning is noise by tonight. The interviewer wants recency decay, an engagement-versus-quality value model, and an answer for filter bubbles, not just retrieval plus ranking.