A Comparative Study of Music Recommendation Systems

Technology in the music players is developing rapidly, especially in smart phones. Nowadays users have access to millions of songs available online. Selecting favorite music among these large archives is one of the biggest problem. Every user has his own taste of music selection. Selecting music depends on the surroundings and the mood of the user. New users and new items emerge every day, and the system has to react to them promptly. The problem of personalized music recommendation that takes different kinds of auxiliary information into consideration is resource constraint due to large amount of data involvement but these models provide much accurate results so more of these are being used for commercial purpose. The main aim of the recommendation system is to recommend songs such that it is closed to the user's choice. As a comparative study, we will be analyzing the Graph-based Novelty Research On The Music Recommendation, Music Recommendation System Based on the Continuous Combination of Contextual Information, Smart-DJ: Context-aware Personalizing for Music Recommendation on Smart phones. These models are outlined to assist the users to find out the new music that is personalized. For the analysis purpose, we will be using data set provided by Douban Music.