Ensemble Learning for Cross-Selling Using Multitype Multiway Data

Cross-selling is an integral component of customer relationship management. Using relevant information to improve customer response rate is a challenging task in cross-selling. Incorporating multitype multiway customer behavioral, including related product, similar customer and historical promotion, data into cross-selling models is helpful in improving the classification performance. Customer behavioral data can be represented by multiple high-order tensors. Most existing supervised tensor learning methods cannot directly deal with heterogeneous and sparse multiway data in cross selling. In this study, two novel ensemble learning methods, multiple kernel support tensor machine (MK-STM) and multiple support vector machine ensemble (M-SVM-E), are proposed for crossselling using multitype multiway data. The MK-STM and the M-SVM-E can also perform feature selections from large sparse multitype multiway data. Based on these two methods, collaborative and non-collaborative ensemble learning frameworks are developed. In these frameworks, many existing classification and ensemble methods can be combined for classification using multitype multiway data. Computational experiments are conducted on two databases extracted from open access databases. The experimental results show that the MK-STM exhibits the best performance and has better performance than existing supervised tensor learning methods.