Computational Advertising: Techniques for Targeting Relevant Ads: 26 (Foundations and Trends� in Information Retrieval) - Tapa blanda

Dave, Kushal; Varma, Vasudeva

9781601988324: Computational Advertising: Techniques for Targeting Relevant Ads: 26 (Foundations and Trends� in Information Retrieval)

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ISBN 10: 160198832X ISBN 13: 9781601988324

Editorial: Now Publishers Inc, 2014

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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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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.

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