Architecture of in-app ad recommender system

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dc.contributor.authorMukherjee, Anik
dc.contributor.authorSundarraj, R. P.
dc.contributor.authorDutta, Kaushik
dc.contributor.editorParsons, Jeffrey
dc.contributor.editorTuunanen, Tuure
dc.contributor.editorVenable, John R.
dc.contributor.editorHelfert, Markus
dc.contributor.editorDonnellan, Brian
dc.contributor.editorKenneally, Jim
dc.contributor.funderNational University of Ireland, Maynoothen
dc.contributor.funderClaremont Graduate University, United Statesen
dc.contributor.funderMemorial University of Newfoundland, Canadaen
dc.date.accessioned2016-05-16T09:24:21Z
dc.date.available2016-05-16T09:24:21Z
dc.date.issued2016-05
dc.description.abstractIncreased adoption of smartphones has caused mobile advertising to be the secondmost revenue-generating medium among all forms of existing online advertising. Application (henceforth called app) developers try to monetize their apps by selling in-app ad-spaces to the advertisers (or ad-agencies) through various intermediaries such as ad-networks. Surveys, however, indicate that mobile ad campaigns are not as successful as they can be, in part due to inappropriate audience targeting, and in turn, user-apathy toward such ads. This motivates the need for a system, where both advertisers and mobile-app developers gain from the in-app advertising eco-system. In this paper, we propose an architecture of design-science artifacts for an ad-network, to meet the objectives of both these stakeholders.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMukherjee, A., Sundarraj, R. P. & Dutta, K. 2016. Architecture of in-app ad recommender system. In: Parsons, J., Tuunanen, T., Venable, J. R., Helfert, M., Donnellan, B., & Kenneally, J. (eds.) Breakthroughs and Emerging Insights from Ongoing Design Science Projects: Research-in-progress papers and poster presentations from the 11th International Conference on Design Science Research in Information Systems and Technology (DESRIST) 2016. St. John, Canada, 23-25 May. pp. 59-67en
dc.identifier.endpage66en
dc.identifier.isbn978-1-906642-85-3
dc.identifier.startpage59en
dc.identifier.urihttps://hdl.handle.net/10468/2567
dc.language.isoenen
dc.publisherDESRIST 2016en
dc.relation.ispartofBreakthroughs and Emerging Insights from Ongoing Design Science Projects: Research-in-progress papers and poster presentations from the 11th International Conference on Design Science Research in Information Systems and Technology (DESRIST) 2016. St. John, Canada, 23-25 May
dc.relation.urihttps://desrist2016.wordpress.com/
dc.rights©2016, The Author(s).en
dc.subjectNegotiationen
dc.subjectRecommender systemen
dc.subjectInteger programming modelen
dc.titleArchitecture of in-app ad recommender systemen
dc.typeConference itemen
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