Matching and Ranking with Hidden Topics towards Online Contextual Advertising

Authors:
Dieu-Thu Le;Cam-Tu Nguyen;Quang-Thuy Ha;Xuan-Hieu Phan;Susumu Horiguchi
Affiliations:
-;-;-;-;-
Venue:
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2008

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Abstract

In online contextual advertising, ad messages are displayed related to the content of the target Web page. It leads to the problem in information retrieval community: how to select the most relevant ad messages given the content of a page. To deal with this problem, we propose a framework that takes advantage of large scale external datasets. This framework provides a mechanism to discover the semantic relations between Web pages and ad messages by analyzing topics for them. This helps overcome the problem of mismatch due to unimportant words and the difference in vocabularies between Web pages and ad messages. The framework has been evaluated through a number of experiments. It shows a significant improvement in accuracy over word/lexicon-based matching and ranking methods.