Patent ID: 6937994
Filing Date: 2005-08-30
Classification: G06Q

Abstract:
1. A method of using a computer for targeting products and promotions to candidate sets of customers having attributes, said method iteratively implementing phases with each phase comprising the steps of: a) storing unlabeled customer data in a storage device, each unlabeled customer data having one or more customer attributes; b) implementing a means for selecting a subset of unlabeled customer data from said storage device, said selecting means responsive to received guessed labels generated for unlabeled customer data instances in said selected subset according to a first classification method and, further responsive to weights computed for unlabeled data instances using said guessed labels; c) implementing a means for labeling the selected subset of unlabeled customer data using external information and adding said labeled data subset to a labeled data set, said labeled data set comprising one or more labeled data instances; d) implementing a model generator device for retrieving said labeled data set and generating one or more classification models, said customer classification model generating comprising steps of: f) applying one or more generated classification models M(r) and said guessed labels for unlabeled data instances to compute said weights in step b); and utilizing said weights for selecting a next subset from remaining unlabeled data stored in said storage device in a subsequent phase; and, g) repeating step b) through f) in each phase until a termination criterion is satisfied; and h) implementing a device for combining each of said generated one or more classification models M(r) into a resultant classifier model, said resultant classifier model adapted to determine suitability of potential customers for receiving targeted products and promotions, wherein said resultant classifier model is based on a reduced amount of labeled data set instances with increased classification accuracy.