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Predicting the Click-Through Rate for Rare/New AdsAuthors: Kushal Dave,Vasudeva Varma Date: 2010-04-23 Report no: IIIT/TR/2010/15 AbstractSponsored search has quickly became the largest source of revenue for Web search engines. Search engines generate revenue from click/impression events on ads. Click on an ad depend heavily on the rank at which the ad is displayed on the search page. The ordering of ads on the search page is done based on the historical click information. Hence accurately predicting the click-through rate (CTR) of an ad is of paramount importance for maximizing the revenue. We first consider the problem of removing the inherent presentation and position bias from the click-through logs for the already established ads. For newly created ads or rare ads we do not have sufficient historical information to calculate their CTR values. We present a model that inherits the click information of rare/new ads from other frequent ads which are semantically related. The semantic features are derived from the query ad click-through graphs and advertisers account information. We use gradient boosted decision trees (GBDT) as a regression model. Experiments show that the model learned using these features gives a very good prediction for the CTR values of the ads. Improvements obtained in the paper are found significant at 99% significance level. Full report: pdf Centre for Search and Information Extraction Lab |
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