成果速递：IOM实验室提出一种用于消费者购买路径预测的动态超图神经网络

Title: Dynamic hypergraph neural networks for consumer purchase path prediction: Integrating promotions, experiences, and store heterogeneity

Authors: Wu Juntao; Liu Quan; Chen Lifan; Zhao Wenxiang; Liu Hefu

Journal: Decision Support Systems

URL: doi.org/10.1016/j.dss.2026.114614

abstract: 

With the rise of mobile and online food and beverage services, consumer behavior has become increasingly dynamic, multichannel, and personalized. To support improved decision making, restaurant operators need systems that not only predict purchasing behavior but also capture the complex interactions among promotions, consumer experiences, and historical actions. This paper proposes a Behavioral Economics-informed Hyper Graph Network (BEHGN) framework, which integrates Expected Utility Theory and Mental Accounting Theory to model both short-term promotional responses and long-term experience effects. BEHGN employs a hypergraph structure to represent consumers, stores, products, and coupons, and uses large language models to extract experiential features from online reviews. This design enables the system to capture cross-store behavior dynamics and store heterogeneity in a unified decision support model. Experiments on real-world data from two major food and beverage chains demonstrate that BEHGN outperforms existing models in predicting consumer purchase paths, offering higher accuracy and adaptability. The results highlight the potential of BEHGN to enhance food and beverage decision support, contributing to better strategy formulation and performance outcomes.