Paper Contents
Abstract
Using machine learning, one can create a system that recommends songs or playlists to users based on their listening habits, preferences, and other pertinent information. The intention is to improve the user's musical experience by offering tailored suggestions. This paper offers a method for music recommendation that makes use of machine learning techniques to provide personalized recommendations based on user preferences and context. This study develops a comprehensive recommendation strategy by combining content-based and collaborative filtering. Collaborative filtering algorithms determine the mood and emotion of users by examining their listening habits and tastes, and then suggest songs that fit those moods and emotions. The technology offers a dynamic platform for people to discover, enjoy, and interact with music that speaks to their individual likes and moods by combining collaborative, content-based, and machine learning features.
Copyright
Copyright © 2023 Swathi Panchireddy, Nagamani Yanda. This is an open access article distributed under the Creative Commons Attribution License.
