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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Grbovic, M. Malkin, J. Das, H. |
| Copyright Year | 2013 |
| Description | Author affiliation: Yahoo! Labs., Sunnyvale, CA, USA (Grbovic, M.; Malkin, J.; Das, H.) |
| Abstract | Late web display advertisements are problematic for both the user experience and the monetary machinery powering the display advertising industry. If a web page is delivered to a user but the ad fails to load in time, the publisher cannot charge the advertiser for that impression. Detecting whether a specific ad will render in time could give the publisher a choice to show that ad or another one. Further, discovering the root causes of latency, possibly over time as new violators emerge, would allow the publisher to address the actionable issues. We propose a system that predicts, at serve time, which ads are likely to have high latency. Once identified we can either ignore those ads, even if they win the auction, or apply a penalty to those ads. In addition, our system collects the daily impression logs, consisting of different types of observations measured at serve time and the associated latency in milliseconds, and analyzes the data to identify the features associated with late ads and likely to be causing the delay. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 762 |
| Ending Page | 767 |
| File Size | 159632 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479912933 |
| DOI | 10.1109/BigData.2013.6691649 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Classification Predictive models Feature extraction Data models Vectors Mapreduce Big data Browsers Ad latency Logistics |
| Content Type | Text |
| Resource Type | Article |
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