Hierarchical feature selection and predictive modeling for estimating performance metrics

Abstract

A bid management system generates estimated performance metrics at the bid unit level to facilitate bid optimization. The bid management system includes a hierarchical feature selection and prediction approach. Feature selection is performed by aggregating historical performance metrics to a higher hierarchical level and testing features for statistical significance. Features for which a significance level satisfies a significance threshold are selected for prediction analysis. The prediction analysis uses a statistical model based on selected features to generate estimated performance metrics at the bid unit level. In some implementations, the prediction analysis uses a hierarchical Bayesian smoothing method in which estimated performance metrics are calculated at the bid unit level using a posterior probability distribution derived from a prior probability distribution determined based on aggregated performance metrics and a likelihood function that takes into account historical performance metrics from the bid unit level based on the selected features.