Optimizing Agentic AI Architectures for Personalized Product Recommendations Using Real-Time Behavioral Data in Retail Media Platforms
Keywords:
Agentic AI, Personalized Recommendations, Retail Media, Real-Time Data, Behavioral Modeling, Reinforcement Learning, Dynamic Systems, Click-Through Rate (CTR), Recommendation Architecture, User Intent ModelingAbstract
Personalized product recommendation systems have become central to retail media platforms, yet conventional algorithms struggle with dynamic and real-time consumer behaviors. This study proposes an agentic AI architecture that leverages real-time behavioral data to optimize personalized recommendations at scale. By integrating reinforcement learning agents with modular decision nodes, the system continuously adapts to user interactions, preferences, and temporal patterns. Results from simulated retail datasets demonstrate an improvement in click-through rate (CTR) by 17.4% and basket size by 11.2% over traditional recommendation engines. This architecture advances current recommender systems through adaptability, personalization, and computational efficiency.
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