Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models
Michal Aharon, Amit Kagian, Oren Somekh · 2017
Yahoo's native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. OFFSET is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, OFFSET includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal.