Dynamic Hierarchical Empirical Bayes: A Predictive Model Applied to Online Advertising
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Population Empirical Bayes
Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does not match the data, predictive accuracy suffers. We develop population empirical Bayes (POP-EB), a hierarchic…
Bayesian InferenceregressionVariational InferencePopulation Predictive Checks
Bayesian modeling helps applied researchers articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers can now e…
Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces
The Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN) classifier is a semi-naive Bayesian model that learns a type of hierarchical redundancy-free tree-like feature representation to estimate the da…
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models provide a rich family of choices for learning and predicting dyadic data between two sets of entities. It generalizes matrix factorization to a supervised learning problem that utilizes attri…
Bayesian InferenceCollaborative FilteringBayesian hierarchical stacking: Some models are (somewhere) useful
Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inpu…
Bayesian InferenceTime SeriesTime Series Analysis