A personalized music recommendation system using convolutional neural networks approach

Shun Hao Chang; Ashu Abdul; Jenhui Chen; Hua Yuan Liao

doi:10.1109/ICASI.2018.8394293

A personalized music recommendation system using convolutional neural networks approach

Shun Hao Chang, Ashu Abdul, Jenhui Chen^*, Hua Yuan Liao

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

37 Scopus citations

Abstract

In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user. The log file contains the history of all users who use the PMRS. The PMRS extracts the user's history from the log file and recommends music under each genre. We use the million song dataset (MSD) to evaluate the PMRS. To show the working of the PMRS, we developed a mobile application (an Android version). We used the confidence score metrics for different music genre to check the performance of the PMRS.

Original language	English
Title of host publication	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
Editors	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	47-49
Number of pages	3
ISBN (Electronic)	9781538643426
DOIs	https://doi.org/10.1109/ICASI.2018.8394293
State	Published - 22 06 2018
Event	4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan Duration: 13 04 2018 → 17 04 2018

Publication series

Name	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference	4th IEEE International Conference on Applied System Innovation, ICASI 2018
Country/Territory	Japan
City	Chiba
Period	13/04/18 → 17/04/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

CNN and music recommendation
Collaborative filtering

Access to Document

10.1109/ICASI.2018.8394293

Cite this

Chang, S. H., Abdul, A., Chen, J., & Liao, H. Y. (2018). A personalized music recommendation system using convolutional neural networks approach. In A. D. K.-T. Lam, S. D. Prior, & T.-H. Meen (Eds.), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018 (pp. 47-49). (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2018.8394293

Chang, Shun Hao ; Abdul, Ashu ; Chen, Jenhui et al. / A personalized music recommendation system using convolutional neural networks approach. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 47-49 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

@inproceedings{2026a6b6f9714eb7a8cfab64ef8256a5,

title = "A personalized music recommendation system using convolutional neural networks approach",

abstract = "In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user. The log file contains the history of all users who use the PMRS. The PMRS extracts the user's history from the log file and recommends music under each genre. We use the million song dataset (MSD) to evaluate the PMRS. To show the working of the PMRS, we developed a mobile application (an Android version). We used the confidence score metrics for different music genre to check the performance of the PMRS.",

keywords = "CNN and music recommendation, Collaborative filtering",

author = "Chang, {Shun Hao} and Ashu Abdul and Jenhui Chen and Liao, {Hua Yuan}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th IEEE International Conference on Applied System Innovation, ICASI 2018 ; Conference date: 13-04-2018 Through 17-04-2018",

year = "2018",

month = jun,

day = "22",

doi = "10.1109/ICASI.2018.8394293",

language = "英语",

series = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018",

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Chang, SH, Abdul, A, Chen, J & Liao, HY 2018, A personalized music recommendation system using convolutional neural networks approach. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 47-49, 4th IEEE International Conference on Applied System Innovation, ICASI 2018, Chiba, Japan, 13/04/18. https://doi.org/10.1109/ICASI.2018.8394293

A personalized music recommendation system using convolutional neural networks approach. / Chang, Shun Hao; Abdul, Ashu; Chen, Jenhui et al.
Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 47-49 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Abdul, Ashu

AU - Chen, Jenhui

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N2 - In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user. The log file contains the history of all users who use the PMRS. The PMRS extracts the user's history from the log file and recommends music under each genre. We use the million song dataset (MSD) to evaluate the PMRS. To show the working of the PMRS, we developed a mobile application (an Android version). We used the confidence score metrics for different music genre to check the performance of the PMRS.

AB - In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user. The log file contains the history of all users who use the PMRS. The PMRS extracts the user's history from the log file and recommends music under each genre. We use the million song dataset (MSD) to evaluate the PMRS. To show the working of the PMRS, we developed a mobile application (an Android version). We used the confidence score metrics for different music genre to check the performance of the PMRS.

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Chang SH, Abdul A, Chen J, Liao HY. A personalized music recommendation system using convolutional neural networks approach. In Lam ADKT, Prior SD, Meen TH, editors, Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 47-49. (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). doi: 10.1109/ICASI.2018.8394293

A personalized music recommendation system using convolutional neural networks approach

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Keywords

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