WCSE

A Time-aware Multi-task Learning Model for Customer Value Prediction in Civil Aviation

Abstract— The precise prediction of customer value is essential for any successful dynamic customer relationship management (CRM) system. It is also the key for the company to maximizing customer returns. In this research, we concentrate on two main aspects of the work in civil aviation field. Firstly, a reasonable value model is the premise of this prediction issue. Therefore, we propose a parametric customer value model RFUM to estimate customer value in civil aviation. It evaluates customer value from four different attributes and then presents customer value by the weight of the attributes. Secondly, Time-aware Multi-task Value Prediction (TMVP) model is proposed to predict the future value of customer. It employs two supervisory signals of purchase propensity and customer value to better train a specific neural network to automatically learn features. Experiments demonstrate that the RFUM model can more accurately measure the value of customer in civil aviation market and the TMVP model can achieve a more precise regression prediction result. In addition, we also find that increasing the time of a single calculation window can improve the performance markedly.

Index Terms— customer value, value prediction, multi-task learning, civil aviation.

Haofei Yang, Youfang Lin, Zhihao Wu, Yiji Zhao
Beijing Key Lab of Traffic Data Analysis and Mining, CHINA
Youfang Lin, Zhihao Wu
Key Lab of Intelligent Passenger Service of Civil Aviation, CHINA
Haofei Yang, Youfang Lin, Zhihao Wu, Yiji Zhao
School of Computer and Information Technology, Beijing Jiaotong University, CHINA

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Cite: Haofei Yang, Youfang Lin, Zhihao Wu, Yiji Zhao, "A Time-aware Multi-task Learning Model for Customer Value Prediction in Civil Aviation," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 588-598, Hong Kong, 15-17 June, 2019.