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

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.