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Predicting TV VIewers' Gender Using TV Watching History

byIISLab 2013.11.27 09:44
저자 Heera Kim, Jaedong Lee, and Jee-Hyong Lee 
학회명 International Symposium on Advanced Intelligent Systems 
학회명 (약자) ISIS 2013 
pp. 90-97 
학회시작일 2013-11-13 
학회종료일 2013-11-16 
비고  

Abstract.

Accurate prediction of demographic attributes is valuable for marketing, personalization and legal investigation. However, most people tend to dislike
exposing their personal information to the service providers because of the private issues. Since these tendencies cause difficulties for gathering personal
information, we need to pay more attention to the latent information. In this paper, we focus on predicting TV viewer’s gender using one’s TV watching history as one of the latent information. We predict one’s gender by training a supervised machine learning algorithm; furthermore we extend the  experiments under the various conditions for analyzing TV watching history data.

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