Automatic Persistent Personalization of Ads in Tourism Websites
Abstract
Information and Communication Technologies (ICT) have dramatically increased the ability of advertisers to target advertising campaigns and make sure that ads are shown to only certain targeted groups of people. Usage of appropriate ads to each visitor may increase Click Through Rates (CTR) and chances of conversion. This paper presents a novel online advertising approach for automatic “persistent personalization” of Web ads on the basis of Web-mining techniques that combine representative parameters for advertising in a unique platform. The functionality of the approach as well as the problems that arose during the implementation are posed and discussed. Finally, the recommendation system has been successfully validated in a travel blog Website. The implemented prototype made it possible to serve the appropriate ads to the targeted audience on the basis of the classification of user profiles. The obtained CTR was the double of the expected common CTR rates in online advertising campaigns.
BIB_text
title = {Automatic Persistent Personalization of Ads in Tourism Websites},
pages = {17-30},
keywds = {
Ad personalization,Persistent personalization,Supervised learning,Implicit user profiling,Hybrid recommender system
}
abstract = {
Information and Communication Technologies (ICT) have dramatically increased the ability of advertisers to target advertising campaigns and make sure that ads are shown to only certain targeted groups of people. Usage of appropriate ads to each visitor may increase Click Through Rates (CTR) and chances of conversion. This paper presents a novel online advertising approach for automatic “persistent personalization” of Web ads on the basis of Web-mining techniques that combine representative parameters for advertising in a unique platform. The functionality of the approach as well as the problems that arose during the implementation are posed and discussed. Finally, the recommendation system has been successfully validated in a travel blog Website. The implemented prototype made it possible to serve the appropriate ads to the targeted audience on the basis of the classification of user profiles. The obtained CTR was the double of the expected common CTR rates in online advertising campaigns.
}
isbn = {978-3-319-28230-5},
date = {2016-01-23},
year = {2016},
}
