NDLI: Computational advertising: leveraging user interaction & contextual factors for improved ad retrieval & ranking

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Computational advertising: leveraging user interaction & contextual factors for improved ad retrieval & ranking

Content Provider	ACM Digital Library
Author	Dave, Kushal S.
Abstract	Computational advertising, popularly known as Online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the web. It 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 attacked in computational advertising (CA) is of the match making between the ads and the context. Based on the context, CA can be broadly compartmentalized into following three areas: Sponsored search, Contextual advertising and Social advertising. Sponsored search refers to the placement of ads on search results page. Contextual advertising deals with matching advertisements to the third party web pages. We refer the placements of ads on a social networking page, leveraging user's social contacts as social advertising. My research work aims at leveraging various user interactions, ad and advertiser related information and contextual information for these three areas of advertising. The research work focuses on the identification of various factors that contribute in retrieving and ranking the most relevant set of ads that match best with the context. Specifically, information associated with the user, publisher and advertiser is leveraged for this purpose.
Starting Page	349
Ending Page	354
Page Count	6
File Format	PDF
ISBN	9781450306379
DOI	10.1145/1963192.1963342
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Direct marketing Contextual advertising Viral marketing Social networks Advertising Sponsored search
Content Type	Text
Resource Type	Article

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