A SVM ensemble learning method using tensor data: An application to cross selling recommendation

Abstract

In many applications such as dynamic social network and customer behavioral analysis, the data intrinsically have many dimensions and can be naturally represented as high-order tensors. In this study, a SVM ensemble learning method is proposed for classification using tensor data. The method is used in identifying cross selling opportunities to recommend personalized products and services to customers. Two real-world databases are used to evaluate the performance of the method. Computational results show that the SVM ensemble learning method has good performance on these databases.