PhD Dissertation Defense by REN Jing | Music Popularity, Diffusion and Recommendation in Social Networks: A Fusion Analytics Approach

Music Popularity, Diffusion and Recommendation in Social Networks:

A Fusion Analytics Approach

About The Talk

The rapid evolution of contemporary digital entertainment and Internet technology has dramatically changed the way people produce and consume music. Although people are less likely to buy CDs now, they nevertheless are listening more than ever to streaming music. Streaming music services, like Spotify and Last.fm, bridge an easy way between the music consumers and massive music products. At the same time, they bring challenge but also chance for the music industry to design for promotion strategies via the new channels. The music industry not only need to understand their music products, they also need to understand the streaming platforms and their consumers. How to make these three key elements collaborate each other harmoniously is still an open question for streaming music ecosystem.

The availability of proprietary corporate and online public data and the development of innovative technology methods now allow us to explore about streaming music ecosystem in more detail from a new and more complete perspective. This dissertation research applies the fusion analytics strategy to explore music popularity, diffusion, and promotion in the streaming music context from a new and effective perspective. It constructs an explanatory cycle of insights, ranging from understanding the business value of music, to how music diffusion works, to how music-related information can be used to promote and recommend music products in streaming music settings.

This dissertation consists of three essays that lie in the interdisciplinary area of information, technology, and business sustainability and value in the music industry. Essay 1 investigates the determinants of music track popularity and patterns, and their impacts on music popularity development on streaming platforms. Essay 2 examines the impacts of external information on music diffusion in a semi-closed social network environment. Essay 3 leverages the learned insights in Essay 1 and 2, and proposes a streaming music promotion and recommendation method by considering two-sided value: music consumers and music providers. This dissertation contributes to understanding the new channels for music popularity and diffusion from a complete perspective, and also paves the way for promoting music in streaming scenarios in ways that go beyond traditional music recommendation.

Speaker Biography

Jing REN is a PhD candidate in SIS, SMU. She works under the guidance of Professor Robert J. Kauffman. Her research focus on the interdisciplinary area of Data Management & Analytics and Information Systems, involving social media analysis, data mining, consumer behavior, business intelligence, and recommender systems. Her research articles appear in both IS and CS related journal and conferences (ECRA, ECIS, PACIS, WWW, MMM, etc.). She obtained her Master and Bachelor of Engineering from Hefei University of Technology, China, in 2011 and 2006. From 2015 to 2016, she was a visiting Ph.D. student at Carnegie Mellon University, Heinz College.