An Intelligent System for Personalized Music Assistance and Predictive Recommendations on Spotify

This work is centered on creating an intelligent recommender system for Spotify users that examines the relationship between users and searched songs. The system is supposed to use the user’s preferences as a basis of personalization to provide suggestions. The implementation makes use of a proof of concept with Python’s Spotify library and the Spotify Web API for developers. Also, it adds supporting libraries like NumPy, Pandas, and OS for data analysis and system functionality. Music tracks and albums are retrieved via the unique six-character universal resource identifier (URI). The essence of this recommender system is to serve user specific requirements in the highly competitive music industry. This translates to the means of listening to music using modern frameworks and sophisticated algorithms. This work focuses on creating an adaptive model meant for fun seeking users that use digital music streaming technologies.