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

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  • Fan, Zhi-Ping
  • Sun, Minghe

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.

Suggested Citation

  • Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
    • Handle: RePEc:eee:ejores:v:255:y:2016:i:1:p:110-120
    DOI: 10.1016/j.ejor.2016.05.020
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    Cited by:

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    4. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.

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