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 recommendations. 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 recommendations. In this study, a novel collaborative ensemble learning method, multi-kernel support tensor machine (MK-STM), is proposed for classification in cross-selling recommendations using multitype multiway customer behavioral data. The MK-STM can also perform feature selections from large sparse multitype multiway data. Computational experiments are conducted using two databases. The experimental results show that the MK-STM has better performance than existing ensemble learning, supervised tensor learning and other commonly used recommendation methods for cross-selling recommendations.
['Zhen Yu Chen', 'Zhi Ping Fan', 'Minghe Sun']
A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations