%0 Conference Paper %A Wu, Hao %A Qiu, Guang %A He, Xiaofei %A Shi, Yuan %A Qu, Mingcheng %A Shen, Jing %A Bu, Jiajun %A Chen, Chun %B 18th International World Wide Web Conference %C Madrid, Spain %D 2009 %F www2009:128 %P 1095-1095 %T Advertising Keyword Generation Using Active Learning %U http://www2009.eprints.org/128/ %X 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 significantly with little user effort required.