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

Zhuolun Li, Robert J. Kauffman, Bing Tian Dai

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This research blends machine learning-based discovery of preference patterns that uses natural language processing for TV viewing data with explanatory modeling that uses econometrics, as a basis for understanding TV viewing preferences at the household-level. We employ a dataset of about 1.1 million observations that was collected via set-top box technology that tracked household-level consumption of the content of its channel subscription package. The data describe the details of what households watched on TV, including the channels and shows, start times and durations, and overall viewing times for content from different digital entertainment genres. This research demonstrates the efficacy of our machine learning and explanatory econometrics approach, and presents insights on consumer behavior and content bundling that are useful for firm strategy in digital entertainment services.
Original languageEnglish
Title of host publicationProceedings of the 50th Annual Hawaii International Conference on System Sciences, HICSS 2017
EditorsTung X. Bui, Ralph Sprague
Number of pages6
Place of PublicationHonolulu
PublisherHawaii International Conference on System Sciences (HICSS)
Publication date2017
Pages3889-3894
ISBN (Electronic)9780998133102
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event50th Annual Hawaii International Conference on System Sciences, HICSS 2017 - Waikoloa Village, United States
Duration: 4 Jan 20177 Jan 2017
Conference number: 50
http://hicss.hawaii.edu/

Conference

Conference50th Annual Hawaii International Conference on System Sciences, HICSS 2017
Number50
Country/TerritoryUnited States
CityWaikoloa Village
Period04/01/201707/01/2017
Internet address
SeriesProceedings of the Annual Hawaii International Conference on System Sciences
Volume2017-January
ISSN1530-1605

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