Abstract
In today’s digital era, the overwhelming volume of available literature necessitates a more personalized approach to book recommendations. Our research aims to address this need by pioneering a solution that integrates hybrid filtering methodologies and advanced chatbot technology driven by machine learning algorithms. By leveraging these cutting-edge techniques, we aspire to redefine the book discovery experience, providing tailored recommendations that resonate with individual preferences. Through the fusion of popularized, collaborative and content-based alongside the capabilities of machine learning models for natural language processing, our system not only enhances recommendation accuracy but also fosters real-time interaction, ultimately enhancing user satisfaction and engagement in the realm of literature. Key Words- Personalized book recommendations, Hybrid filtering methodologies, chatbot technology, machine learning algorithms, Collaborative filtering, Content-based filtering, Natural language processing, Recommendation accuracy.
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