Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising

TL;DR

This paper introduces a Dynamic Hierarchical Empirical Bayesian model that dynamically determines data hierarchies to improve prediction accuracy and efficiency in online advertising metrics, effectively addressing data sparsity issues.

Contribution

The paper presents a novel data-driven hierarchical Bayesian model that adapts hierarchy structures dynamically for better predictions in sparse online advertising data.

Findings

Outperforms existing models in accuracy and efficiency

Effective in both simulated and real-world datasets

Enables real-time predictions with a two-phase system

Abstract

Predicting keywords performance, such as number of impressions, click-through rate (CTR), conversion rate (CVR), revenue per click (RPC), and cost per click (CPC), is critical for sponsored search in the online advertising industry. An interesting phenomenon is that, despite the size of the overall data, the data are very sparse at the individual unit level. To overcome the sparsity and leverage hierarchical information across the data structure, we propose a Dynamic Hierarchical Empirical Bayesian (DHEB) model that dynamically determines the hierarchy through a data-driven process and provides shrinkage-based estimations. Our method is also equipped with an efficient empirical approach to derive inferences through the hierarchy. We evaluate the proposed method in both simulated and real-world datasets and compare to several competitive models. The results favor the proposed method among all comparisons in terms of both accuracy and efficiency. In the end, we design a two-phase system to serve prediction in real time.