GenAI-Driven Java Framework for Personalized Customer Experience in E-Commerce

Personalization in e-commerce formerly depended on rule-based engines and collaborative filtering. However, these methods sometimes have difficulty with scalability, contextual awareness, and most importantly customer trust. Generative AI (GenAI) is coming into its own now, therefore fresh opportunities, have appeared for designing adaptable and justifiable personalization systems. A GenAI-driven Java framework that brings together natural language processing, sentiment analysis, conversational AI, and explainability modules for real-time personalization in online retail is introduced within this very paper. A simulation that included $\mathbf{1 0 0 0 0}$, users and lasted more than ninety days evaluates the framework as contrasted against rule-based, collaborative filtering, plus hybrid systems. Results actually point out marked increases with customer engagement at more than thirty eight percent and sales conversion which saw an increase of just over thirty nine percent plus an appreciable satisfaction rate showing over nineteen percent; revenue made per visitor going beyond thirty-five percent moreover and retention showing around fifteen-percent increases. In fact it is a success story. Besides gains achieved from its accuracy the framework enhances important components namely, trust levels explainability, with added fairness resulting showing increases that add a final boost to ROI showing over nine percent as grams. We're presented with a large potential, when using GenAI to lift the consumer experience and at the same time ensuring that the benefits are measurable.