OPTIMIZING AN AI-DRIVEN CHATBOT THROUGH NATURAL LANGUAGE PROCESSING AND REAL-TIME FEEDBACK FOR PERSONALIZED RECOMMENDATIONS
Keywords:
Natural Language Processing (NLP), Optimized chatbots, Real-time FeedbackAbstract
In recent years, the application of artificial intelligence (AI) in the field of recommendation systems has gained significant traction due to its ability to handle complex and non-linear data. This paper explores the optimization of an AI-driven chatbot designed to provide personalized recommendations. By leveraging Natural Language Processing (NLP) and real-time feedback mechanisms, the chatbot continuously learns and adapts to user preferences, enhancing its recommendation accuracy. The study demonstrates how integrating these technologies into the chatbot's architecture can improve user satisfaction and interaction efficiency. The results indicate a significant enhancement in the chatbot's ability to offer tailored suggestions, thereby underscoring the potential of AI-driven systems in personalized user experiences.
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