document: Using the wisdom of the crowds for keyword generation In the sponsored search model , search engines are paid by businesses that are interested in displaying ads for their site alongside the search results . Businesses bid for keywords , and their ad is displayed when the keyword is queried to the search engine . An important problem in this process is ` keyword generation ' : given a business that is interested in launching a campaign , suggest keywords that are related to that campaign . We address this problem by making use of the query logs of the search engine . We identify queries related to a campaign by exploiting the associations between queries and URLs as they are captured by the user 's clicks . These queries form good keyword suggestions since they capture the `` wisdom of the crowd '' as to what is related to a site . We formulate the problem as a semi-supervised learning problem , and propose algorithms within the Markov Random Field model . We perform experiments with real query logs , and we demonstrate that our algorithms scale to large query logs and produce meaningful results .
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