Agentic Personalization in E-Commerce: From Reactive Recommendation Engines to Goal-Driven Consumer Agents
Keywords:
Agentic personalization, E-commerce recommender systems, Consumer agents, Reinforcement learning, Intelligent recommendation systems, Digital commerce personalizationAbstract
Personalization has become a central capability in modern e-commerce platforms, enabling digital marketplaces to provide tailored product recommendations that enhance user experience and commercial performance. Traditional recommender systems primarily operate using reactive mechanisms that analyze historical user interactions, browsing patterns, and purchase behaviors to generate suggestions. Although these systems have significantly improved product discovery and engagement, they often struggle to capture evolving user intentions, contextual preferences, and long-term decision goals. As digital commerce ecosystems become increasingly complex, there is a growing need for more intelligent personalization strategies capable of proactive assistance. This study explores the emerging paradigm of agentic personalization, which represents a shift from conventional recommendation engines toward autonomous, goal-driven consumer agents. Unlike traditional systems that passively respond to past interactions, agentic personalization systems actively interpret user objectives, reason about possible actions, and optimize recommendations to achieve long-term user satisfaction. The research proposes a conceptual architecture that integrates user goal inference, contextual knowledge modeling, and reinforcement learning based policy optimization to support intelligent consumer agents within e-commerce environments. Experimental evaluation using simulated large-scale interaction datasets compares traditional collaborative filtering models, deep neural recommendation architectures, and the proposed agentic framework. The results demonstrate that agent-driven personalization significantly improves recommendation accuracy, user engagement levels, and conversion rates compared with conventional reactive recommendation approaches. These findings highlight the potential of goal-driven consumer agents to transform the next generation of adaptive and intelligent digital commerce systems.
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