Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Stochastic variability in sponsored search auctions: observations and models
Proceedings of the 12th ACM conference on Electronic commerce
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
Psychological advertising: exploring user psychology for click prediction in sponsored search
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling click and relevance relationship for sponsored search
Proceedings of the 22nd international conference on World Wide Web companion
Query clustering based on bid landscape for sponsored search auction optimization
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Information graph model and application to online advertising
Proceedings of the 1st workshop on User engagement optimization
Exploiting contextual factors for click modeling in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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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.