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Click-Through Rate Estimation for Rare Events in Online Advertising
| Content Provider | CiteSeerX |
|---|---|
| Author | Wang, Xuerui Li, Wei Cui, Ying Zhang, Ruofei Bruce Mao, Jianchang |
| Abstract | In online advertising campaigns, to measure purchase propensity, click-through rate (CTR), defined as a ratio of number of clicks to number of impressions, is one of the most informative metrics used in business activities such as performance evaluation and budget planning. No matter what channel an ad goes through (display ads, sponsored search or contextual advertising), CTR estimation for rare events is essential but challenging, often incurring with huge variance, due to the sparsity in data. In this chapter, to alleviate this sparsity, we develop models and methods to smoothen CTR estimation by taking advantage of the data hierarchy in nature or by clustering and data continuity in time to leverage information from data close to the events of interest. In a contextual advertising system running at Yahoo!, we demonstrate that our methods lead to significantly more accurate estimation of CTRs. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Rare Event Online Advertising Click-through Rate Estimation Ctr Estimation Online Advertising Campaign Click-through Rate Contextual Advertising System Informative Metric Contextual Advertising Display Ad Business Activity Budget Planning Purchase Propensity Performance Evaluation Data Hierarchy Accurate Estimation Data Continuity Huge Variance |
| Content Type | Text |
