Position-normalized click prediction in search advertising

Abstract

Click-through rate (CTR) prediction plays a central role in search advertising. One needs CTR estimates unbiased by positional effect in order for ad ranking, allocation, and pricing to be based upon ad relevance or quality in terms of click propensity. However, the observed click-through data has been confounded by positional bias, that is, users tend to click more on ads shown in higher positions than lower ones, regardless of the ad relevance. We describe a probabilistic factor model as a general principled approach to studying these exogenous and often overwhelming phenomena. The model is simple and linear in nature, while empirically justified by the advertising domain. Our experimental results with artificial and real-world sponsored search data show the soundness of the underlying model assumption, which in turn yields superior prediction accuracy.