Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41630

Can I See Beyond What You See? Blending Machine Learning and Econometrics to Discover Household TV Viewing Preferences

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Title: Can I See Beyond What You See? Blending Machine Learning and Econometrics to Discover Household TV Viewing Preferences
Authors: Li, Zhuolun
Kauffman, Robert
Dai, Bingtian
Keywords: Data analytics
econometrics
household preferences
latent Dirichlet allocation
TV viewing preferences
Issue Date: 04 Jan 2017
Abstract: This article discusses the emergence of a computational social science analytics fusion as a mainstream scientific approach involving machine-based methods and explanatory empiricism as a basis for the discovery of new policy-related insights for business, consumer and social settings. It reflects the interdisciplinary background of the new approaches that the Hawaii International Conference on Systems Science has embraced over the years, and especially some of the recent development and shifts in the scientific study of technology-related phenomena. It also has evoked new forms of research inquiry, blended approaches to research methodology, and more pointed interest in the production of research results that have direct application in various industry contexts. We review background knowledge to showcase the methods shifts, and demonstrate the new forms of research, by showcasing contemporary applications that will be interesting to the audience on the occasion of the HICSS 50th anniversary.
Pages/Duration: 6 pages
URI/DOI: http://hdl.handle.net/10125/41630
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.471
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Electronic Marketing Minitrack



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