Batch-Mode Computational Advertising Based on Modern Portfolio Theory

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.

Authors and Affiliations

1. Birkbeck, University of London, London, WC1E 7HX, UK

   Dell Zhang & Jinsong Lu

Editors and Affiliations

1. Department of Computing Science, Sir Alwyn Williams Building, Lilybank Gardens, University of Glasgow, G12 8QQ, Glasgow, Scotland, UK

   Leif Azzopardi

2. Microsoft Research Ltd, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK

   Gabriella Kazai  & Stephen Robertson  & 

3. Knowledge Media Institute,, The Open University, MK7 6AA, Milton Keynes, UK

   Stefan Rüger

4. Microsoft Research Ltd, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, United Kingdom

   Milad Shokouhi  & Emine Yilmaz  & 

5. School of Computing, The Robert Gordon University, St Andrew Street, AB25 1HG, Aberdeen, UK

   Dawei Song

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