Hybrid Recommender System for Music Information Retrieval

Abstract

Music plays an integral role in our lives as the most popular type of recreation. With the advent of new technologies such as Internet and portable media players, large amount of music data is available online which can be distributed and easily made available to people. Enormous amount of music data is released every year by several artists with songs varying in features, genre and so on. Because of this, a need for reliable and easy access of songs based on user preferences is necessary. The recommender system focuses on generating playlists based on the physical, perceptual and acoustical properties of the song (content based filtering approach), or on commonalities between users on a particular basis like ratings or user data history (collaborative filtering). The system thus developed is a hybrid music recommender tool which creates a user centric suggestion system accompanied by feature extraction which in turn enhances the accuracy of music recommendations.