Predicting Click Through Rate for Job Listings

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

by Manish Gupta

Webpage: http://www2009.eprints.org/107/1/p1053.pdf

Click Through Rate (CTR) is an important metric for ad systems, job portals, recommendation systems. CTR impacts publisher's revenue, advertiser's bid amounts in "pay for performance" business models. We learn regression models using features of the job, optional click history of job, features of "related" jobs. We show that our models predict CTR much better than predicting avg. CTR for all job listings, even in absence of the click history for the job listing.

Keywords: Poster Session


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/107


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