Using the wisdom of the crowds for keyword generation Export

@inproceedings{fuxman2008using, abstract = {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.}, address = {New York, NY, USA}, author = {Fuxman, Ariel and Tsaparas, Panayiotis and Achan, Kannan and Agrawal, Rakesh}, booktitle = {WWW '08: Proceeding of the 17th international conference on World Wide Web}, citeulike-article-id = {2775010}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1367497.1367506}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1367497.1367506}, comment = {Keyword generation by propagating in the bipartite click graph. A group of URLs (sites) are picked as seeds from a category, the seeds from other categories are taken as negative examples, and a random walk is executed.}, doi = {10.1145/1367497.1367506}, isbn = {978-1-60558-085-2}, keywords = {advertising, queries}, location = {Beijing, China}, pages = {61--70}, posted-at = {2008-07-10 11:02:27}, priority = {0}, publisher = {ACM}, title = {Using the wisdom of the crowds for keyword generation}, url = {http://dx.doi.org/10.1145/1367497.1367506}, year = {2008} }

TY - CONF ID - fuxman2008using L3 - citeulike-article-id:2775010 N2 - 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. CY - Beijing, China SN - 978-1-60558-085-2 AD - New York, NY, USA EP - 70 TI - Using the wisdom of the crowds for keyword generation PB - ACM T2 - WWW '08: Proceeding of the 17th international conference on World Wide Web SP - 61 KW - advertising KW - queries N1 - Keyword generation by propagating in the bipartite click graph. A group of URLs (sites) are picked as seeds from a category, the seeds from other categories are taken as negative examples, and a random walk is executed. AU - Fuxman, Ariel AU - Tsaparas, Panayiotis AU - Achan, Kannan AU - Agrawal, Rakesh PY - 2008/// UR - http://dx.doi.org/10.1145/1367497.1367506 ER -