Text mining in computational advertising

Computational advertising uses information on web‐browsing activity and additional covariates to select advertisements for display to the user. The statistical challenge is to develop methodology that matches ads to users who are likely to purchase the advertised product. These methods not only invo...

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Bibliographic Details
Published in:Statistical analysis and data mining 2013-08, Vol.6 (4), p.273-285
Main Authors: Soriano, Jacopo, Au, Timothy, Banks, David
Format: Article
Language:English
Subjects:
LDA
LSI
networks
recommender system
text mining
topic model
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Summary:Computational advertising uses information on web‐browsing activity and additional covariates to select advertisements for display to the user. The statistical challenge is to develop methodology that matches ads to users who are likely to purchase the advertised product. These methods not only involve text mining, but also may draw upon additional modeling related to both the user and the advertisement. This paper reviews various aspects of text mining, including n‐grams, topic modeling, and text networks, and discusses different strategies in the context of specific business models. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013
ISSN:1932-1864
1932-1872
DOI:10.1002/sam.11197