Online Advertising Using Linguistic Knowledge



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.


Average Precision Name Entity Recognition Translation Model Linguistic Knowledge Average Recall 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Dipartimento di Scienze della ComunicazioneUniversità degli Studi di SalernoSalernoItaly
  2. 2.Department of Management Information SystemsUniversity of HaifaHaifaIsrael

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