title: Predicting Click Through Rate for Job Listings
creator: Gupta, Manish
description: 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.
date: 2009-04
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
identifier: http://www2009.eprints.org/107/1/p1053.pdf
identifier: Gupta, Manish <http://www2009.eprints.org/view/author/Gupta=3AManish=3A=3A.html> (2009) Predicting Click Through Rate for Job Listings. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain.
relation: http://www2009.eprints.org/107/