Chatbot: Music Recommendation System

Abstract

This paper presents the development of a Chatbot Music Recommendation System that utilizes artificial intelligence to enhance user engagement through personalized music suggestions. The system operates on two primary mechanisms: passive recommendations based on contextual factors such as time of day and weather conditions, and interactive suggestions during user engagement. By integrating real-time data from external APIs, the chatbot can provide relevant song recommendations tailored to the user's environment, such as upbeat tracks for sunny days or calming melodies for rainy weather. Additionally, the system employs natural language processing techniques to understand user preferences and emotions, allowing it to refine its recommendations over time. A comprehensive evaluation of the system's performance is conducted through user feedback and interaction metrics, demonstrating its effectiveness in improving the music discovery experience. The findings indicate that the chatbot not only enhances user satisfaction but also fosters a deeper emotional connection with music. This research contributes to the field of music recommendation systems by merging technology with user-centric design, ultimately transforming how individuals interact with music in their daily lives