Advertising keyword generation using active learning

Authors:
Hao Wu;Guang Qiu;Xiaofei He;Yuan Shi;Mingcheng Qu;Jing Shen;Jiajun Bu;Chun Chen
Affiliations:
College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;China Disabled Persons' Federation Information Center, Beijing, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Venue:
Proceedings of the 18th international conference on World wide web
Year:
2009

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Abstract

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.