Reliable gender prediction based on users' video viewing behavior

Document Type

Conference Proceeding

Publication Date

7-2-2016

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.

Identifier

85014544142 (Scopus)

ISBN

[9781509054725]

Publication Title

Proceedings IEEE International Conference on Data Mining Icdm

External Full Text Location

https://doi.org/10.1109/ICDM.2016.19

ISSN

15504786

First Page

649

Last Page

658

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