Computational Statistics & Data Analysis - Nonlinear methods and data mining
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
A collaborative filtering approach to ad recommendation using the query-ad click graph
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 20th international conference companion on World wide web
Proceedings of the fifth ACM international conference on Web search and data mining
Multimedia features for click prediction of new ads in display advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
CTR prediction for contextual advertising: learning-to-rank approach
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Up or Down? Click-Through Rate Prediction from Social Intention for Search Advertising
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Exploiting contextual factors for click modeling in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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Ads on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information of rare/new ads from other semantically related ads. The semantic features are derived from the query ad click-through graphs and advertisers account information. We show that the model learned using these features give a very good prediction for the CTR values.