Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/WIIAT.2008.180 | Web Intelligence |
Keywords | Field | DocType |
web page,large scale,advertisement message,ad message,online contextual advertising,topic analysis,external datasets,information retrieval,vocabulary,semantic relation,pattern matching,target web page,hidden topic matching,lexicon-based matching,information retrieval community,internet,ranking method,hidden topics,hidden topic ranking,contextual advertising,advertising data processing,relevant ad message,web advertising,web pages,machine learning,context modeling,advertising | Contextual advertising,Information retrieval,Web page,Ranking,Computer science,Context model,Lexicon,Vocabulary,Pattern matching,The Internet | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3496-1 | 2 |
PageRank | References | Authors |
0.40 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dieu-Thu Le | 1 | 68 | 4.85 |
| Cam-Tu Nguyen | 2 | 139 | 12.40 |
| Quang-Thuy Ha | 3 | 219 | 27.89 |
| Xuan-Hieu Phan | 4 | 322 | 21.37 |
| Susumu Horiguchi | 5 | 1002 | 113.41 |