Ad click prediction
H. Brendan McMahan, Gary David Holt, D. Sculley, MICHAEL SEAN YOUNG, Dietmar Ebner, Julian Grady, Lan Nie, Todd W. Phillips, Eugene V. Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, MARTIN P. WATTENBERG, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica · 2013
Predicting ad click-through rates (CTR) is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. We present a selection of case studies and topics drawn from recent experiments in the setting of a deployed CTR prediction system. These include improvements in the context of traditional supervised learning based on an FTRL-Proximal online learning algorithm (which has excellent sparsity and convergence properties) and the use of per-coordinate learning rates.