Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Improving ad relevance in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
The anatomy of an ad: structured indexing and retrieval for sponsored search
Proceedings of the 19th international conference on World wide web
Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-objective optimization for sponsored search
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
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Recently there has been a surge in research that predicts retrieval relevance using historical click-through data. While a larger number of clicks between a query and a document provides a stronger ``confidence" of relevance, most models in the literature that learn from clicks are error-prone as they do not take into account any confidence estimates. Sponsored Search models are especially prone to this error as they are typically trained on search engine logs in order to predict click-through-rate (CTR). The estimated CTR ultimately determines the rank at which an ad is shown and also impacts the price (cost-per-click) for the advertiser. In this paper, we improve a model that applies collaborative filtering on click data by training a filter that has been trained to predict pure relevance. Applying the filter to ads that have seen few clicks on live traffic results in improved CTR and click-yield (CY). Additionally, in offline experiments we find that using features based on the \emph{organic} results improves the relevance based filter's performance.