TY - GEN
T1 - Multiword keyword recommendation system for online advertising
AU - Thomaidou, Stamatina
AU - Vazirgiannis, Michalis
PY - 2011/9/20
Y1 - 2011/9/20
N2 - As search engines, social networks, and the World Wide Web become more popular and widely used, online advertising turns into a very profitable industry. Individuals and companies promote their products or services in search engines through textual ads, alongside the organic search results triggered by a specific query. For this purpose, advertisers must create advertising campaigns. The development of these campaigns is a laborious task involving significant human resources and expertise. In this paper we propose a system for multiword keyword recommendations in the context of developing a web advertising campaign in a semiautomatic manner. Given a landing page, the system extracts relevant terms consisted of two or three words to match a potential search query. Furthermore, it proposes the most relevant keywords and other suggested terms that do not exist in the landing page text using search result snippets. In addition, we present blind testing experiments on real world data indicating that our approach outperforms prominent existing industrial solutions in most of the cases.
AB - As search engines, social networks, and the World Wide Web become more popular and widely used, online advertising turns into a very profitable industry. Individuals and companies promote their products or services in search engines through textual ads, alongside the organic search results triggered by a specific query. For this purpose, advertisers must create advertising campaigns. The development of these campaigns is a laborious task involving significant human resources and expertise. In this paper we propose a system for multiword keyword recommendations in the context of developing a web advertising campaign in a semiautomatic manner. Given a landing page, the system extracts relevant terms consisted of two or three words to match a potential search query. Furthermore, it proposes the most relevant keywords and other suggested terms that do not exist in the landing page text using search result snippets. In addition, we present blind testing experiments on real world data indicating that our approach outperforms prominent existing industrial solutions in most of the cases.
KW - Keyword selection
KW - Online advertising
KW - Sponsored search
KW - Textual advertising
U2 - 10.1109/ASONAM.2011.70
DO - 10.1109/ASONAM.2011.70
M3 - Conference contribution
AN - SCOPUS:80052794861
SN - 9780769543758
T3 - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
SP - 423
EP - 427
BT - Proceedings - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
T2 - 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011
Y2 - 25 July 2011 through 27 July 2011
ER -