Advertising Keyword Generation Using Active Learning

Presented at: 18th International World Wide Web Conference (WWW2009)

by Hao Wu, Guang Qiu, Xiaofei He, Yuan Shi, Mingcheng Qu, Jing Shen, Jiajun Bu, Chun Chen

Webpage: http://www2009.eprints.org/128/1/p1095.pdf

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.

Keywords: Poster Session


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/128


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