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dc.contributor.authorShih, Wen-Yuehen_US
dc.contributor.authorLu, Yi-Shuen_US
dc.contributor.authorTsai, Hsiao-Pingen_US
dc.contributor.authorHuang, Jiun-Longen_US
dc.date.accessioned2020-10-05T02:02:04Z-
dc.date.available2020-10-05T02:02:04Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2020.3016824en_US
dc.identifier.urihttp://hdl.handle.net/11536/155487-
dc.description.abstractThe bidding strategy plays the most important role to help the Demand Side Platforms (DSPs) making bidding decisions on a large number of bid requests in Real Time Bidding (RTB) to satisfy the different objectives of campaigns under the lifetime and budget constraints. In this paper, we focus on branding campaign whose objective is to obtain as many impressions as possible under the lifetime and budget constraints. To achieve the objectives of branding campaigns, we propose a novel expected win rate-based bidding strategy for branding campaign under the lifetime and budget constraints by utilizing a model-free reinforcement learning model. Specifically, to prevent missing good opportunities resulting from submitting extremely low bid prices, the concept of the base winning price is introduced to determine the lower bound of expected winning price. In addition, to obtain more impressions, the concept of the DSP-specified budget spending plan is proposed to determine the proper winning prices. The base expected win rate is then calculated based on the base winning price and the winning price determined by the DSP-specified budget spending plan. Since RTB is a dynamic environment, we propose a novel expected win rate-based bidding strategy named EWDQN which utilizes Deep Q Network (DQN) to dynamically determine the expected win rate according to the base expected win rate and the current status of the RTB market, and then determines the bid price according to the expected win rate. To the best of our knowledge, this is the first research applying the reinforcement learning technique on the bidding strategies for branding campaign. To measure the performance of EWDQN, several experiments are conducted on two real datasets. Experimental results show that EWDQN outperforms the-state-of-the-art bidding strategies for branding campaign in terms of the number of obtained impressions and CPM (cost per thousand impressions).en_US
dc.language.isoen_USen_US
dc.subjectAdvertisingen_US
dc.subjectPredictive modelsen_US
dc.subjectAdaptation modelsen_US
dc.subjectReal-time systemsen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectLogisticsen_US
dc.subjectComputer scienceen_US
dc.subjectReal time biddingen_US
dc.subjectonline advertisingen_US
dc.subjectbidding strategyen_US
dc.subjectreinforcement learningen_US
dc.subjectdemand side platformen_US
dc.subjectbranding campaignen_US
dc.titleAn Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Modelen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2020.3016824en_US
dc.identifier.journalIEEE ACCESSen_US
dc.citation.volume8en_US
dc.citation.spage151952en_US
dc.citation.epage151967en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000564184100001en_US
dc.citation.woscount0en_US
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