document: Contextual advertising by combining relevance with click feedback Contextual advertising supports much of the Web 's ecosystem today . User experience and revenue ( shared by the site publisher and the ad network ) depend on the relevance of the displayed ads to the page content . As with other document retrieval systems , relevance is provided by scoring the match between individual ads ( documents ) and the content of the page where the ads are shown ( query ) . In this paper we show how this match can be improved significantly by augmenting the ad-page scoring function with extra parameters from a logistic regression model on the words in the pages and ads . A key property of the proposed model is that it can be mapped to standard cosine similarity matching and is suitable for efficient and scalable implementation over inverted indexes . The model parameter values are learned from logs containing ad impressions and clicks , with shrinkage estimators being used to combat sparsity . To scale our computations to train on an extremely large training corpus consisting of several gigabytes of data , we parallelize our fitting algorithm in a Hadoop framework ( 10 ) . Experimental evaluation is provided showing improved click prediction over a holdout set of impression and click events from a large scale real-world ad placement engine . Our best model achieves a 25 % lift in precision relative to a traditional information retrieval model which is based on cosine similarity , for recalling 10 % of the clicks in our test data .
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