Deep Contextual Situational Analytics For Intelligent User Behaviour Prediction In Smart Environments

Understanding user behaviour in dynamic environments has become an important challenge in modern intelligent systems. Traditional user modelling approaches often rely on static information and fail to capture the influence of changing contextual situations such as location, time, activity, and environmental conditions. To address this limitation, this paper presents an intelligent context-driven situational analytics framework designed to analyse and model user behaviour in smart environments. The proposed approach integrates contextual data collection, situation identification, and behavioural analysis to dynamically interpret user activities and preferences. By utilizing contextual parameters and adaptive analytical techniques, the framework enables systems to better understand user needs and provide more accurate and personalized responses. The model improves decision making by continuously analysing situational patterns and predicting possible user actions. Experimental evaluation demonstrates that incorporating situational context significantly enhances the effectiveness of user behaviour prediction compared to traditional static user models. The proposed framework can be applied in various smart applications including personalized recommendation systems, intelligent assistants, and context-aware services.