Mobile users behaviour prediction using machine learning

Abstract

The rapid integration of mobile technology into everyday life has created unprecedented opportunities and challenges in understanding and predicting mobile user behavior. This paper offers a comprehensive survey of various machine learning techniques employed to analyze and predict behaviors of mobile users, providing crucial insights for enhancing user interaction and service delivery. The study systematically reviews traditional algorithms, ensemble methods, and advanced deep learning models, evaluating their efficacy in different scenarios. Key aspects such as feature engineering, model selection, and the critical ethical considerations involved in predictive analytics are thoroughly explored. The research highlights the significant implications of predictive models in optimizing resource allocation, improving targeted marketing strategies, and enhancing the overall user experience. Through this survey, we demonstrate the transformative potential of machine learning in mobile user behavior prediction and outline future research directions to address the emerging challenges in this dynamic field. The results show promising avenues for the practical application of these technologies, fostering a deeper understanding of mobile user behaviors and paving the way for innovative personalized services. The paper not only underscores the versatility and power of machine learning in mobile user behavior analysis but also addresses the ethical dimensions of data usage, emphasizing the need for models that respect user privacy and data security. This comprehensive survey serves as a foundational text for researchers, industry professionals, and technologists eager to explore the intersections of mobile technology, machine learning, and user behavior analytics.