Abstract
With continuous expansion of music resources there was a need for an increase in the rate of searching similar artists which previously consumed a lot of effort as well as time. An automated software was much needed for this which helped in almost instantaneous song recommendation based on similar artists as well as top songs. This article presents a music recommendation using both popularity (Collaborative filtering) and similarity-based (Content-based filtering) approach. In this music recommendation system, uses a well-known filtering method, i.e. content based which will recommend the based on their previously listened and searched songs. Songs will be recommended based on genre, artists and also uses collaborative filtering which will recommend the top ten popular songs from the new releases. The log file contains users’ history. To evaluate the music recommendation system, the million song data set (MSD) was used in the system. To show the working of the music recommendation system, a Web App is developed.
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
Dolatkia I, Azimzadeh F (2016) Music recommendation system based on the continuous combination of contextual information 2016. In: 2nd international conference on web research (ICWR), Tehran, Iran, pp 108–114. https://doi.org/10.1109/ICWR.2016.7498454
Wenzhen W (2019) Personalized music recommendation algorithm based on hybrid collaborative filtering technology. 2019. In: International conference on smart grid and electrical automation (ICSGEA), Xiangtan, China, pp 280–283. https://doi.org/10.1109/ICSGEA.2019.00071
Chang S, Abdul A, Chen Z, Liao H (2018) A personalized music recommendation system using convolutional neural networks approach. In: 2018 IEEE international conference on applied system invention (ICASI), Chiba, Japan, pp 47–49. https://doi.org/10.1109/ICASI.2018.8394293
Aljanaki A, Wiering F, Remco C, Veltkamp (2016) Studying emotion induced by music through a crowdsourcing game. Inf Process Manag 52(1):115–128. ISSN 0306–4573, https://doi.org/10.1016/j.ipm.2015.03.004
Markus S (2019) Deep learning in music recommendation systems. J Front Appl Math Stat 5:44. https://www.frontiersin.org/article/10.3389/fams.2019.00044, https://doi.org/10.3389/fams.2019.00044, ISSN 2297–4687
Turnip R, Nurjanah D, Kusumo DS (2007) Hybrid recommender system for learning material using content-based filtering and collaborative filtering with good learners’ rating. In: 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), Miri, Malaysia, pp 61–66. https://doi.org/10.1109/IC3e.2017.8409239
Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. The adaptive web: methods and strategies of web personalization. pp 291–324
Hyung Z, Park JS, Lee K (2017) Utilizing context-relevant keywords extracted from a large collection of user-generated documents for music discovery. Info Proces Manag 53(5):1185–1200
Pazzani MJ, Billsus D (2007) Content based recommendation systems. The adaptive web: methods and strategies of web personalization. pp 325–341
Dataset: http://millionsongdataset.com/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Budhani, S., Kataria, R., Nagdev, M., Niranjani, S., Saindane, P. (2022). Moelleux—Music Recommendation System. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_34
Download citation
DOI: https://doi.org/10.1007/978-981-16-6460-1_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6459-5
Online ISBN: 978-981-16-6460-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)