Reliable gender prediction based on users' video viewing behavior

Jie Zhang, Kuang Du, Ruihua Cheng, Zhi Wei, Chenguang Qin, Huaxin You, Sha Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

With the growth of the digital advertising market, it has become more important than ever to target the desired audiences. Among various demographic traits, gender information plays a key role in precisely targeting the potential consumers in online advertising and ecommerce. However, such personal information is generally unavailable to digital media sellers. In this paper, we propose a novel task-specific multi-Task learning algorithm to predict users' gender information from their video viewing behaviors. To detect as many desired users as possible, while controlling the Type I error rate at a user-specified level, we further propose Bayes testing and decision procedures to efficiently identify male and female users, respectively. Comprehensive experiments have justified the effectiveness and reliability of our framework.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages649-658
Number of pages10
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jan 31 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
CountrySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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