Ad click prediction
2013pp. 1222–1230
Citations Over TimeTop 1% of 2013 papers
H. Brendan McMahan, Gary D. Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica
Abstract
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.
Related Papers
- → The impact of visual appearance on user response in online display advertising(2012)31 cited
- → A Survey of Online Advertising Click-Through Rate Prediction Models(2020)19 cited
- The Extended Advertising Network Model(2010)
- Techniques for estimating click-through rates of Web advertisements:A survey(2013)
- → Click-Through Rate (Ctr) Prediction for Online Advertising(2020)