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Computational Advertising: Techniques for Targeting Relevant Ads
Click to zoom the image Computational Advertising: Techniques for Targeting Relevant Ads
Publisher:Now Publishers
Author: Dave, Kushal; Varma, Vasudeva;
Publication Date:2014-10-31
ISBN-10:160198832X
ISBN-13:9781601988324
Number of Pages:106 Pages
Book Type:Paperback
Weight:254 gram
Books Dimensions:234 x 156 x 9 mm
Format of ebooks: PDF(Acrobat Reader) or Word version doc Document
Brief introduction of ebooks
Computational Advertising: Techniques for Targeting Relevant Ads


Computational advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular content on the web. This survey focuses on discussing the problems and solutions to retrieve and rank relevant ads using information from the content, publisher, user and advertiser.
Abstract
Computational Advertising (CA), popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean the content where the ad is shown, the user who is viewing the ad, or the social network of the user. CA is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search, and text analysis. The core problem addressed in CA is of match-making between the ads and the context. Research in CA has evolved considerably over the last decade and a half and currently continues both in traditional areas such as vocabulary mismatch, query rewriting, and click prediction, and recently identified areas like user targeting, mobile advertising, and social advertising. Computational Advertising: Techniques for Targeting Relevant Ads focuses predominantly on the problems and solutions proposed in traditional areas while also looking briefly at the emerging areas in the latter half of the monograph. To facilitate future research, a discussion of available resources, a list of public benchmark datasets and a discussion on future research directions are provided in the concluding sections.

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