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 ena-bling reduced cost of an ad campaign.

Online Advertising Using Linguistic Knowledge

D'AVANZO, ERNESTO;ELIA, Annibale
2011-01-01

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 ena-bling reduced cost of an ad campaign.
2011
9783790826319
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/3015885
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