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.
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White, T., Salehi-Abari, A., Box, B. (2010). On How Ants Put Advertisements on the Web. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_51
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DOI: https://doi.org/10.1007/978-3-642-13025-0_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13024-3
Online ISBN: 978-3-642-13025-0
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