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User Query Enhancement for Behavioral Targeting

User Query Enhancement for Behavioral Targeting

Wei Xiong, Y. F. Brook Wu
Copyright: © 2017 |Pages: 21
ISBN13: 9781522520580|ISBN10: 1522520589|EISBN13: 9781522520597
DOI: 10.4018/978-1-5225-2058-0.ch009
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MLA

Xiong, Wei, and Y. F. Brook Wu. "User Query Enhancement for Behavioral Targeting." Ontologies and Big Data Considerations for Effective Intelligence, edited by Joan Lu and Qiang Xu, IGI Global, 2017, pp. 413-433. https://doi.org/10.4018/978-1-5225-2058-0.ch009

APA

Xiong, W. & Wu, Y. F. (2017). User Query Enhancement for Behavioral Targeting. In J. Lu & Q. Xu (Eds.), Ontologies and Big Data Considerations for Effective Intelligence (pp. 413-433). IGI Global. https://doi.org/10.4018/978-1-5225-2058-0.ch009

Chicago

Xiong, Wei, and Y. F. Brook Wu. "User Query Enhancement for Behavioral Targeting." In Ontologies and Big Data Considerations for Effective Intelligence, edited by Joan Lu and Qiang Xu, 413-433. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2058-0.ch009

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Abstract

Ad targeting has been receiving more and more attention in the online publishing world, where advertisers want their ads to be seen by potential consumers at the right time. This chapter aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a user intent representation strategy and a query enhancement mechanism. The authors focus on investigating the intent based user classification performance and the effectiveness of user segmentation under a topic model that helps explore semantic relation between user queries in behavioral targeting. In addition, the authors propose an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized.

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