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MCGM: A multi-channel CTR model with hierarchical gated mechanism for precision marketing

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Abstract

Intelligent finance is a new form of business with deep integration of artificial intelligence technology and financial industry. An important application of intelligent finance is the precise marketing of financial products. As a key link in precision marketing, click through rate(CTR) prediction has made great progress, but there is still room for improvement in multiple features fusion, feature interactions learning and other aspects. In view of these needs and challenges, we propose a CTR prediction model named MCGM, which is used to realize precision marketing of financial products. The main characteristics of the model are as follows: (i) in order to effectively fuse multiple features, we design a hierarchical gated mechanism to select salient feature information at different levels; (ii) in order to fully learn the nonlinear relationship between features, we design a multi-channel feature interactions learning module. Specifically, it adopts factorization machine(FM), improved CrossNet(ICN) and multilayer perceptron(MLP) components to model the feature interactions from high-order to low-order, in order to obtain the abstract features containing rich information. Comprehensive and sufficient experiments on real world datasets show that the proposed model achieves better prediction performance compared with baselines. The proposed model not only has specific application value in the field of financial products marketing, but also provides an idea reference for data-driven marketing modeling.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets/Bank+Marketing

  2. http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/

  3. https://www.kaggle.com/c/santander-product-recommendation

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful reviews, which are very helpful on the revision of this paper. This work is supported by National Natural Science Foundation of China (NO.72261003, 62276196), Guizhou Provincial Science and Technology Project (NO.Qiankehejichu-ZK[2022]yiban019, [2019]5103) and Scientific Research Project of Qiannan Normal University for Nationalities (NO.QNSY2018JS010).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Guizhou University of Finance and Economics, Huaxi University Town, Guiyang, 550025, Guizhou Province, China

    Zilong Jiang

  2. School of Computer and Artificial Intelligence, Wuhan University of Technology, Hongshan District, Wuhan, 430070, Hubei Province, China

    Zilong Jiang & Lin Li

  3. College of Big Data Statistics, Guizhou University of Finance and Economics, Huaxi University Town, Guiyang, 550025, Guizhou Province, China

    Dali Wang

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  1. Zilong Jiang
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Correspondence to Lin Li.

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Jiang, Z., Li, L. & Wang, D. MCGM: A multi-channel CTR model with hierarchical gated mechanism for precision marketing. World Wide Web 26, 2115–2141 (2023). https://doi.org/10.1007/s11280-022-01125-z

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