Thesis Abstract

This project aims to develop a recommendation system for personalized music streaming services, leveraging machine learning and user behavior analysis. The system will utilize collaborative filtering, content-based filtering, and hybrid recommendation techniques to provide users with personalized music recommendations based on their preferences, listening history, and contextual information. The project will contribute to enhancing user engagement and satisfaction in music streaming platforms by delivering tailored music recommendations, thereby addressing the challenge of content discovery in vast music libraries.

Thesis Overview