A framework for intermediated online targeted advertising with banner ranking mechanism

Authors:
Kai Li;Efosa C. Idemudia;Zhangxi Lin;Yang Yu
Affiliations:
Department of Industrial Engineering, Teda College, Nankai University, Tianjin, People's Republic of China 300071;Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, USA;Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, USA;Center for Advanced Analytics and Business Intelligence, Texas Tech University, Lubbock, USA
Venue:
Information Systems and e-Business Management
Year:
2012

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Abstract

Reinforced by the fast growth of electronic commerce, even during the current global economic downturn, intermediated online targeted advertising (IOTA) has emerged as a promising electronic business model empowered by the Web 2.0 principle. IOTA maximizes the profit of online targeted advertising services by displaying the proper banner contents to certain types of Web users in real time in order to increase the click-through rate (CTR). However, due to severe competition in the online advertising market, the principles and algorithms of IOTA remain highly confidential. This paper is intended to unveil the nature of IOTA. We propose an IOTA service system framework and present its implementation scheme. Specifically, we address the advertisement allocation problem, using an advertisement ranking mechanism and considering the ads impression quota and the time-of-day (TOD) effect. Simulation results show that advertisement ranking in a subset of clusters that actively estimates the quota situation is feasible and efficient.