  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.
