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KDD 2020 | Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction

Accepted Papers

Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction

Wenjuan Luo: DiDi Chuxing; Han Zhang: DiDi Chuxing; Xiaodi Yang: DiDi Chuxing; Lin Bo: DiDi Chuxing; Xiaoqing Yang: DiDi Chuxing; Zang Li: DiDi Chuxing; Xiaohu Qie: DiDi Chuxing; Jieping Ye: DiDi Chuxing


Customer response prediction is critical in many industrial applications such as online advertising and recommendations. In particular, the challenge is greater for ride-hailing platforms such as Uber and DiDi, because the response prediction models need to consider historical and real-time event information in the physical environment, such as surrounding traffic and supply and demand conditions. In this paper, we propose to use dynamically constructed heterogeneous graph for each ongoing event to encode the attributes of the event and its surroundings. In addition, we propose a multi-layer graph neural network model to learn the impact of historical actions and the surrounding environment on the current events, and generate an effective event representation to improve the accuracy of the response model. We investigate this framework to two practical applications on the DiDi platform. Offline and online experiments show that the framework can significantly improve prediction performance. The framework has been deployed in the online production environment and serves tens of millions of event prediction requests every day.

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