A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu

Zhi-Ping Fan and Minghe Sun

European Journal of Operational Research, 2016, vol. 255, issue 1, 110-120

Abstract: 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.

Keywords: Data mining; Customer relationship management; Multitype multiway data; Support tensor machine; Cross-selling (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:255:y:2016:i:1:p:110-120

DOI: 10.1016/j.ejor.2016.05.020

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