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dc.contributor.authorClark, Jessica-
dc.contributor.authorPaiement, Jean Francois-
dc.contributor.authorProvost, Foster-
dc.description.abstractTV viewership data available at the individual set-top box level has enabled new methods for estimating the demographics of shows' audiences, but it is impossible to tell with certainty which household members are watching TV in multi-person households. We address this problem through four main contributions. First, we develop a novel method for estimating the likelihood that each individual in a multi-person household is watching. Second, we derive a set of tasks at which models must succeed in order to demonstrate that they have solved the core problem, since there are no ground-truth labels. Third, we evaluate our new method as well as two current state-of-the-art heuristic methods. Fourth, we conduct some example analyses of viewership in the context of living with others. Our solution has implications for advertisers, researchers who seek better understanding TV viewership, and anyone using data generated by shared devices or accounts. A major TV provider is planning on deploying this method for use in their TV ad-targeting system. No personally identifiable information (PII) was gathered or used in conducting this study. To the extent any data was analyzed, it was anonymous and/or aggregated data, consistent with the carrier's privacy policy.en
dc.description.sponsorshipNYU Stern School of Business, AT&T Researchen
dc.titleWho's Watching TV?en
dc.typeWorking Paperen
Appears in Collections:Center for Business Analytics Working Papers

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