Context-Aware Location-Based User Preference Prediction Using Deep Learning and Real-Time Data Analytics

Abstract : In the era of ubiquitous mobile computing and personalized digital services, understanding user preferences in a context-aware manner has become increasingly important. This paper proposes a novel framework for location-based user preference prediction by leveraging deep learning and real-time data analytics. The proposed system integrates spatiotemporal data, contextual factors (such as time of day, weather, and user activity), and user behavioral history to generate accurate and dynamic preference models. A hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is employed to extract spatial features and model sequential patterns in user data. Real-time data processing using a streaming analytics platform ensures that the predictions adapt to current context and changing user behavior. Experimental evaluations on benchmark datasets and real-world scenarios demonstrate the system's superior accuracy and adaptability compared to traditional recommendation algorithms. The proposed approach is applicable to various domains such as smart retail, tourism, and personalized mobile services.

Keyword Context-Aware Computing, Location-Based Services, User Preference Prediction, Deep Learning, CNN-LSTM, Real-Time Data Analytics, Spatiotemporal Modeling, Personalization, Recommender Systems, Smart Environments.