Unintrusive customization techniques for Web advertising
WWW '99 Proceedings of the eighth international conference on World Wide Web
Modern Information Retrieval
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Ant Colony Optimization
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
International Journal of Electronic Commerce
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Search advertising using web relevance feedback
Proceedings of the 17th ACM conference on Information and knowledge management
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Personalized click prediction in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
WSEAS Transactions on Information Science and Applications
On how ants put advertisements on the web
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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Online advertising is the major source of revenue for most web service providers. Displaying advertisements that match user interests will not only lead to user satisfaction, but it will also maximize the revenues of both advertisers and web publishers. Online Advertisement systems use web mining and machine learning techniques to personalize advertisement selection to a particular user based on certain features such as his browsing behavior or demographic data. This paper presents an overview of online advertisement selection and summarizes the main technical challenges and open issues in this field. The paper investigates most of the relevant existing approaches carried out towards this perspective and provides a comparison and classification of these approaches.