Music popularity, diffusion and recommendation in social networks: A fusion analytics approach

Author

Jing REN, Singapore Management UniversityFollow

Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

6-2018

Abstract

Streaming music and social networks offer an easy way for people to gain access to a massive amount of music, but there are also challenges for the music industry to design for promotion strategies via the new channels. My dissertation employs a fusion of machine-based methods and explanatory empiricism to explore music popularity, diffusion, and promotion in the social network context.

Keywords

Fusion Analytics, Econometrics, Machine Learning, Streaming music, Recommendation, Diffusion

Degree Awarded

PhD in Information Systems

Discipline

Music | OS and Networks

Supervisor(s)

KAUFFMAN, Robert John

Publisher

Singapore Management University

City or Country

Singapore

Citation

REN, Jing. Music popularity, diffusion and recommendation in social networks: A fusion analytics approach. (2018).
Available at: https://ink.library.smu.edu.sg/etd_coll/181

Copyright Owner and License

Author

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.