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- W93480304 abstract "Click-through rate is a quantity of interest to online advertisers, search engine optimizers, and sponsored search providers alike. The rate at which users click on advertisements presented to them serves as both a metric for evaluating advertising eectiv eness and a nancial tool for cost and revenue projection. To predict future click-through rate, investigators typically use historical click information since it provides tangible examples of user behavior. In some cases plentiful historical data are available and this method provides a reliable estimate. More often, however, insucien t historical data exists and creative aggregation must ll the gap. In this work, we hypothesize that dieren t terms have an inherently dieren t likelihood of receiving a sponsored click. For example, the search terms digital camera and brain structure clearly express more and less shoppingoriented intent, respectively. We seek to estimate a termlevel click-through rate (CTR) reecting these inherent differences. At times even aggregation to the term level leaves us with insucien t historical data for a conden t estimate, so we also propose the use of clusters of related terms for less frequent, or even completely novel, terms. We reviewed other broad estimates of CTR (rank CTR and query-volume decile CTR) and compared them to the estimates computed using hierarchical clusters of related terms. We found that using historical data aggregated by cluster leads to more accurate estimates on average of term-level CTR for terms with little or no historical data." @default.
- W93480304 created "2016-06-24" @default.
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- W93480304 date "2006-01-01" @default.
- W93480304 modified "2023-10-06" @default.
- W93480304 title "Predicting Click-Through Rate Using Keyword Clusters" @default.
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