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.

Additional Metadata
Keywords Ant Colony Optimization, Collaborative Filtering, Pheromone, Stigmergy
Persistent URL dx.doi.org/10.1007/978-3-642-13025-0_51
Series Lecture Notes in Computer Science
Citation
White, A, Salehi-Abari, A. (Amirali), & Box, B. (Braden). (2010). On how ants put advertisements on the web. In Lecture Notes in Computer Science. doi:10.1007/978-3-642-13025-0_51