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Computational Advertising: Retrieving and Ranking the Top Ads

Computational Advertising: Retrieving and Ranking the Top Ads (Paperback)

Currently unavailable. Foyles can order for despatch to customer when the publisher is re-supplied on 05/03/2015.
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Computational advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular content on the web. It is a scientific subdiscipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in computational advertising (CA) is of match making between the ads and the content also known as content targeting. Content targeting involves retrieving ads for a query on a search page (known as Sponsored Search) or for a third party web-page content (known as Contextual Advertising). Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and web page). Ads being short and written to attract the user, pose challenges like mismatch in the vocabulary of the content and the ad. Besides, as user's probability of examining an ad decreases with ad's position in the ranked list, it is imperative to keep your best ad at the top. Additionally, the fact that money is involved in the ads business poses several challenges like false bidding, click spam, ad spam etc.

There has been a host of research work published in different areas of Computational Advertising addressing various problems discussed above in the last one and a half decade. Our focus in this survey is on discussing the problems and solutions to retrieve and rank relevant ads using information from the content, publisher, user and advertiser. This survey covers techniques and approaches dealing with several of the issues mentioned above. Research in Computational Advertising has been evolving over time, and currently continues both in traditional areas (vocabulary mis-match, query rewriting, click prediction etc.) and in recently identified areas (user targeting, user influence analysis etc.). We predominantly cover the problems and solutions proposed in the traditional areas in detail and briefly touch upon the emerging user targeting in the latter half of the survey. To facilitate future work, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided.

BusinessBusiness & managementE-commerce: business aspectsBusinessBusiness & managementSales & marketingAdvertisingComputing & ITDatabasesInformation retrieval Publisher: now publishers Inc Publication Date: 31/10/2014 ISBN-13: 9781601988324  Details: Type: Paperback Format: Books
Availability: Currently unavailable. Foyles can order for despatch to customer when the publisher is re-supplied on 05/03/2015.  

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