REAL-TIME USER BEHAVIOR TRACKING FOR AI-DRIVEN IN-SESSION PRODUCT RECOMMENDATIONS AND INSIGHTS
DOI:
https://doi.org/10.34218/IJCET_16_02_009Keywords:
Real-time Recommendations, User Behavior Tracking, In-session Analytics, Adaptive AI Systems, Personalization, User Modeling, Session-based RecommendationsAbstract
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
References
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Covington, Paul, Jay Adams, and Emre Sargin. “Deep Neural Networks for YouTube Recommendations.” Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp. 191–198.
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Copyright (c) 2025 Naresh Kumar (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
