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Abstract
This paper presents a novel framework for automatically tracking user behavior within digital products and seamlessly integrating this data into AI systems to generate real-time product recommendations and actionable insights during active user sessions. We address the challenges of data collection latency, privacy preservation, and recommendation relevance by implementing a hybrid tracking system that combines client-side event capturing with server-side processing. Our approach utilizes a lightweight machine learning model that continuously adapts to evolving user preferences within the current session while maintaining computational efficiency. Experimental results across multiple product categories demonstrate significant improvements in user engagement metrics, with a 27% increase in conversion rates and a 32% reduction in session abandonment compared to traditional recommendation systems that rely on historical data alone. CCS Concepts • Information systems → Recommender systems; Personalization; • Human-centered computing → User models; • Computing methodologies → Real-time machine learning
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