This item is a Poster.
- Wu, Hao - Zhejiang University
- Qiu, Guang - Zhejiang University
- He, Xiaofei - Zhejiang University
- Shi, Yuan - Zhejiang University
- Qu, Mingcheng - Zhejiang University
- Shen, Jing - China Disabled Persons' Federation Information Center
- Bu, Jiajun - Zhejiang University
- Chen, Chun - Zhejiang University
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.
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