Cross-Domain Applications of Machine Learning: From Personalized Recommendations to Autonomous Systems
Keywords:
machine learning, cross-domain applications, personalized recommendation, autonomous systems, urban mobility, predictive analyticsAbstract
Machine learning has rapidly evolved into a transformative technology with applications spanning personalized recommendation systems, autonomous systems, urban mobility, life sciences, finance, and social analytics. This review highlights cross-domain methodologies, including data augmentation, model transfer, and multi-modal data fusion, which enable models to generalize across diverse contexts while maintaining high performance. Personalized recommendations leverage sequential behavior and context-awareness to enhance user experiences, while autonomous systems integrate real-time perception, large language models, and predictive analysis to support navigation, programming assistance, and vehicle safety. Urban mobility applications optimize traffic, resource allocation, and sustainability, whereas life sciences, financial analytics, and social studies benefit from graph-based modeling, market prediction, and social behavior analysis. The review concludes by emphasizing the shared principles across domains and the potential for integrating predictive and recommendation models to drive intelligent, adaptive, and human-centered solutions in future cross-domain applications.
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