The research on computational advertising so far has focused on finding the single best ad. However, in many real situations, more than one ad can be presented. Although it is possible to address this problem myopically by using a single-ad optimisation technique in serial-mode, i.e., one at a time, this approach can be ineffective and inefficient because it ignores the correlation between ads. In this paper, we make a leap forward to address the problem of finding the best ads in batch-mode, i.e., assembling the optimal set of ads to be presented altogether. The key idea is to achieve maximum revenue while controlling the level of risk by diversifying the set of ads. We show how the Modern Portfolio Theory can be applied to this problem to provide elegant solutions and deep insights. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Zhang, D., & Lu, J. (2009). Batch-mode computational advertising based on modern portfolio theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5766 LNCS, pp. 380–383). https://doi.org/10.1007/978-3-642-04417-5_44
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