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Full Description
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 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.
Contents
1: Introduction 2: Finding Advertising Keywords on Web Pages 3: Dealing with Short Text in Ads for Contextual Advertising 4: Handling the Short Search Query for Sponsored Search 5: Ad Quality and Spam 6: Ranking Retrieved Ads For Sponsored Search 7: Ranking Ads in Contextual Advertising 8: How much can Behavioral Targeting help Online Advertising? 9: Display Advertising and Real Time Bidding 10: Emerging topics in Computational Advertising 11: Resources 12: Summary and Concluding Remarks. Acknowledgements. References
