Online Advertising Using Linguistic Knowledge

E. D’Avanzo (), T. Kuflik () and A. Elia ()
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E. D’Avanzo: Università degli Studi di Salerno
T. Kuflik: University of Haifa
A. Elia: Università degli Studi di Salerno

A chapter in Information Technology and Innovation Trends in Organizations, 2011, pp 143-150 from Springer

Abstract: Abstract Pay-per-click advertising is one of the most paved ways of online advertising today. However the top ranking keywords are extremely costly. Since search terms have a “long tail” behaviour, they may be used for a more cost-effective way of selecting the right keywords, achieving similar traffic, and reducing the cost considerably. This paper proposes a methodology that, exploiting linguistic knowledge, identifies cost effective bid keyword in the long tail distribution. The experiments show that these keywords are highly relevant (90% average precision) and better targeted than those suggested by other methods, while enabling reduced cost of an ad campaign.

Keywords: Average Precision; Name Entity Recognition; Translation Model; Linguistic Knowledge; Average Recall (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2632-6_17

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DOI: 10.1007/978-3-7908-2632-6_17

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