Efficient Behavior Targeting Using SVM Ensemble Indexing

Behavior targeting (BT) is a promising tool for online advertising. The state-of-the-art BT methods, which are mainly based on regression models, have two limitations. First, learning regression models for behavior targeting is difficult since user clicks are typically several orders of magnitude fewer than views. Second, the user interests are not fixed, but often transient and influenced by media and pop culture. In this paper, we propose to formulate behavior targeting as a classification problem. Specifically, we propose to use an SVM ensemble for behavior prediction. The challenge of using ensemble SVM for BT stems from the computational complexity (it takes 53 minutes in our experiments to predict behavior for 32 million users, which is inadequate for online application). To this end, we propose a fast ensemble SVM prediction framework, which builds an indexing structure for SVM ensemble to achieve sub-linear prediction time complexity. Experimental results on real-world large scale behavior targeting data demonstrate that the proposed method is efficient and outperforms existing linear regression based BT models.