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Rank actionable recommendation model hypotheses with 79-88% compute savings by predicting full-scale performance from small-scale proxies.
Click intent matters—MARCO achieves nearly perfect calibration and boosts conversions by 2.80% by treating clicks as distinct signals rather than uniform events.