Composition-Based Music Recommendation System

Abstract

The increasing popularity of music streaming services has led to a vast amount of music content being available to users. However, this has also created a challenge of how to provide users with personalized music recommendations that meet their specific preferences. To address this challenge, By analyzing the cosine similarity values between the selected song and all other songs in the dataset, the system ranks and presents a list of top recommendations. Each recommendation is accompanied by essential information, including the song name and artist name, making it more user-friendly and informative. The proposed system utilizes preprocessed music features and a cosine similarity matrix that offers a streamlined and efficient solution, demonstrating the potential for effective music recommendations in various contexts. The proposed composition-based music recommendation system offers an innovative and efficient way to provide personalized music recommendations to users. The system provides a more personalized and engaging music experience, which can ultimately lead to increased user satisfaction and retention. The system also has the potential to be integrated with existing music streaming services, thereby offering a valuable tool for music providers to enhance their services and retain users.