TY  - CONF
ID  - www2009107
UR  - http://www2009.eprints.org/107/
A1  - Gupta, Manish
Y1  - 2009/04//
N2  - 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.
TI  - Predicting Click Through Rate for Job Listings
SP  - 1053
M2  - Madrid, Spain
AV  - public
EP  - 1053
T2  - 18th International World Wide Web Conference
ER  -