Abstract
Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma-F1 by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at k, compared to a representative strategy.
| Original language | English |
|---|---|
| Article number | A24 |
| Journal | ACM Transactions on the Web |
| Volume | 7 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2013 Oct 1 |
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Keywords
- Algorithms
- Experimentation
- Performance
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Semantic contextual advertising based on the open directory project. / Lee, Jung Hyun; Ha, Jongwoo; Jung, Jin Yong; Lee, Sang-Geun.
In: ACM Transactions on the Web, Vol. 7, No. 4, A24, 01.10.2013.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Semantic contextual advertising based on the open directory project
AU - Lee, Jung Hyun
AU - Ha, Jongwoo
AU - Jung, Jin Yong
AU - Lee, Sang-Geun
PY - 2013/10/1
Y1 - 2013/10/1
N2 - Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma-F1 by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at k, compared to a representative strategy.
AB - Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma-F1 by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at k, compared to a representative strategy.
KW - Algorithms
KW - Experimentation
KW - Performance
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U2 - 10.1145/2529995.2529997
DO - 10.1145/2529995.2529997
M3 - Article
AN - SCOPUS:84887481113
VL - 7
JO - ACM Transactions on the Web
JF - ACM Transactions on the Web
SN - 1559-1131
IS - 4
M1 - A24
ER -