Probabilistic macro behavioral targeting

Abstract

We investigate a class of emerging online marketing challenges in social networks; and formally, we define macro behavioral targeting (MBT) to be the marketing efforts that appeal to a massive targeted population with non-personalized broadcasting. Upon the problem formulation, we describe a probabilistic graphical model for MBT. In our model, we derive the prior distributions from scratch because existing applications of graphical model / Bayesian network cannot fully capture the unique characteristics of MBT. In the derivation, we propose an approximation method to circumvent an intractable situation where order statistics need be calculated from exponentially increasing computations. In the experiments, we present case studies on real Facebook data.