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Refereed Papers


Track: Internet Monetization: Online Advertising

Paper Title:
Externalities in Online Advertising

Authors:

  • Arpita Ghosh(Yahoo! Research)
  • Mohammad Mahdian(Yahoo! Research)

Abstract:
Most models for online advertising assume that an advertiser's value from winning an ad auction, which depends on the clickthrough rate or conversion rate of the advertisement, is independent of other advertisements served alongside it in the same session. This ignores an important {em externality effect}: as the advertising audience has a limited attention span, a high-quality ad on a page can detract attention from other ads on the same page. That is, the utility to a winner in such an auction also depends on the set of other winners. In this paper, we introduce the problem of modeling externalities in online advertising, and study the winner determination problem in these models. Our models are based on choice models on the audience side. We show that in the most general case, the winner determination problem is hard even to approximate. However, we give an approximation algorithm for this problem with an approximation factor that is logarithmic in the ratio of the maximum to the minimum bid. Furthermore, we show that there are some interesting special cases, such as the case where the audience preferences are single peaked, where the problem can be solved exactly in polynomial time. For all these algorithms, we prove that the winner determination algorithm can be combined with VCG-style payments to yield truthful mechanisms.

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