The rapid expansion of ride-hailing services has generated massive amounts of user travel data, presenting both opportunities and challenges for service optimization. This research proposes a comprehensive framework for identifying user travel preferences and developing personalized recommendation strategies using advanced machine learning techniques. Our methodology integrates feature extraction algorithms, pattern recognition models, and recommendation systems to enhance user experience and operational efficiency. Through extensive experiments on real-world datasets, we demonstrate that our approach achieves 87.3% accuracy in preference identification and improves user satisfaction by 23.7% compared to conventional methods. The proposed framework effectively addresses the heterogeneity of user behaviors while maintaining computational efficiency, providing practical solutions for ride-hailing platforms to deliver customized services and optimize resource allocation.
Xiaotong Shi (Wed,) studied this question.