Architecture of in-app ad recommender system
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Published Version
Date
2016-05
Authors
Mukherjee, Anik
Sundarraj, R. P.
Dutta, Kaushik
Journal Title
Journal ISSN
Volume Title
Publisher
DESRIST 2016
Published Version
Abstract
Increased 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.
Description
Keywords
Negotiation , Recommender system , Integer programming model
Citation
Mukherjee, 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. 59-67
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Copyright
©2016, The Author(s).