TY  - CONF
ID  - www2009128
UR  - http://www2009.eprints.org/128/
A1  - Wu, Hao
A1  - Qiu, Guang
A1  - He, Xiaofei
A1  - Shi, Yuan
A1  - Qu, Mingcheng
A1  - Shen, Jing
A1  - Bu, Jiajun
A1  - Chen, Chun
Y1  - 2009/04//
N2  - This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising. We formulate the ranking of relevant terms as a supervised learning problem and suggest new terms for the seed by leveraging user relevance feedback information. Active learning is employed to select the most informative samples from a set of candidate terms for user labeling. Experiments show our approach improves the relevance of generated terms signi?cantly with little user effort required. 
TI  - Advertising Keyword Generation Using Active Learning
SP  - 1095
M2  - Madrid, Spain
AV  - public
EP  - 1095
T2  - 18th International World Wide Web Conference
ER  -