Library Book Recommendation Chatbot Using Rasa NLU

In response to the growing demand for streamlined library management, this project introduces an innovative system centered around a friendly and intuitive chatbot. The motivation behind this endeavor stems from the need for a cost-effective, personalized recommendation system tailored specifically to local library collections. By harnessing the power of Rasa NLU, a conversational agent was developed to seamlessly assist library visitors in discovering their next favorite read. Through a meticulous integration of Rasa NLU's capabilities, including custom actions for book and author inquiries and entity extraction for genres and descriptions, the chatbot effectively navigates user queries within the library's context. With a keen focus on precision and user engagement, the system's DIETClassifier ensures accurate intent recognition, facilitating seamless interaction and meaningful responses. The project outcomes underscore the chatbot's ability to understand nuanced requests, provide tailored book recommendations, and enhance user experience within library environments. This initiative serves as a testament to the transformative potential of open-source AI tools in enhancing public service offerings and fostering greater accessibility to library resources [1][2][6][8][12].