On how ants put advertisements on the web

Authors:
Tony White;Amirali Salehi-Abari;Braden Box
Affiliations:
School of Computer Science, Carleton University, Ottawa, Ontario, Canada;School of Computer Science, Carleton University, Ottawa, Ontario, Canada;School of Computer Science, Carleton University, Ottawa, Ontario, Canada
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

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Abstract

Advertising is an important aspect of the Web as many services rely on it for continued viability. This paper provides insight into the effectiveness of using ant-inspired algorithms to solve the problem of Internet advertising. The paper is motivated by the success of collaborative filtering systems and the success of ant-inspired systems in solving data mining and complex classification problems. Using the vector space formalism, a model is proposed that learns to associate ads with pages with no prior knowledge of users' interests. The model uses historical data from users' click-through patterns in order to improve associations. A test bed and experimental methodology is described, and the proposed model evaluated using simulation. The reported results clearly show that significant improvements in ad association performance are achievable.