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dc.contributor.authorLai, Hsu-Chaoen_US
dc.contributor.authorShih, Wen-Yuehen_US
dc.contributor.authorHuang, Jiun-Longen_US
dc.contributor.authorChen, Yi-Chengen_US
dc.date.accessioned2018-08-21T05:56:39Z-
dc.date.available2018-08-21T05:56:39Z-
dc.date.issued2016-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/146475-
dc.description.abstractOnline advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.en_US
dc.language.isoen_USen_US
dc.subjectReal Time Biddingen_US
dc.subjectOnline Advertisementen_US
dc.subjectRequest Trafficen_US
dc.subjectDemand Side Platformen_US
dc.titlePredicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platformsen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)en_US
dc.citation.spage3491en_US
dc.citation.epage3498en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000399115003067en_US
Appears in Collections:Conferences Paper