Abstract
With the advent of digital music and music-streaming platforms, the amount of music available for selection is now greater than ever. Sorting through all this music is impossible for anyone. Music recommendation systems reduce human effort by automatically recommending music based on genre, artist, instrument, and user reviews. Although music recommendation systems are widely used commercially, there does not exist any perfect recommendation system that can provide best music recommendation to the user with the minimal user effort. In this paper, we reviewed the various recommendation systems that are currently in use including content-based, collaborative, emotion-based, and other techniques. We have also explored the strengths and weaknesses of each recommendation technique and at the end, we have provided an overview of a music recommendation system that may solve many of the challenges that existing recommendation systems face through an improved hybrid recommendation system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Lee, et al., Music recommendation system based on genre distance and user preference classification. J. Theor. Appl. Inf. Technol. 96(5) (2018)
M. Millecamp, et al., Controlling spotify recommendations: effects of personal characteristics on music recommender user interfaces, in Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, ACM (2018)
J. O’Bryant, A Survey of Music Recommendation and Possible Improvements. (2017)
P. Knees, S. Markus, A survey of music similarity and recommendation from music context data. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 10(1), 2 (2013)
S. Ferretti, Clustering of musical pieces through complex networks: an Assessment over Guitar Solos, in IEEE MultiMedia (2018)
Y Song, D. Simon, P. Marcus, A survey of music recommendation systems and future perspectives, in 9th International Symposium on Computer Music Modeling and Retrieval, vol 4 (2012)
P. Deshmukh, K. Geetanjali, A Survey of Music Recommendation System (2018)
J. Skowronek, M.F. McKinney, S. Van De Par, Ground truth for automatic music mood classification, in ISMIR (2006)
S.E. Park, S. Lee, S. Lee, Session-based collaborative filtering for predicting the next song, in 2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering (CNSI), IEEE (2011)
I. Kamehkhosh, D. Jannach, L. Lerche, Personalized next-track music recommendation with multi-dimensional long-term preference signals, in UMAP (Extended Proceedings) (2016)
M. Schedl, A. Flexer, J. Urbano, The neglected user in music information retrieval research. J. Intell. Inf. Syst. 41(3), 523–539 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Paul, D., Kundu, S. (2020). A Survey of Music Recommendation Systems with a Proposed Music Recommendation System. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_26
Download citation
DOI: https://doi.org/10.1007/978-981-13-7403-6_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7402-9
Online ISBN: 978-981-13-7403-6
eBook Packages: EngineeringEngineering (R0)