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
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
