Predicting click through rate for job listings

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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Measurement, Performance, Experimentation Keywords Prediction, Click Through Rate, jobs, linear regression, CTR, CPC, Treenet, GBDT, gradient boosted decision trees

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Manish S. Gupta
• Keywords: Click Through Rate, CTR Impacts Publisher, Internet Technology, Models Predict Ctr, WWW 2009