Multiview hierarchical bayesian regression model andapplication to online advertising
Tianbing Xu, Ruofei Zhang, Zhen Chao Guo · 2012
With the development of Web applications, large scale data are popular; and they are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about multi-view data with implicit structure. In this paper, we propose a novel hierarchical Bayesian mixture regression model, which discovers and then exploits the relationships among multiple views of the data to perform various machine learning tasks. A stochastic EM inference and learning algorithm is derived; and a parallel implementation in Hadoop MapReduce [9] paradigm is developed to scale up the learning. We apply the developed model and algorithm on click-through-rate (CTR) prediction and campaign targeting recommendation in online advertising to measure its effectiveness. The experiments on both synthetic data and large scale ads serving data from a real world online advertising exchange demonstrate the superior CTR prediction accuracy of our method compared to existing state-of-the-art methods. The results also show that our model can recommend high performance targeting features for online advertising campaigns.