Dynamic Budget Adjustment in Search Auctions
Zhang, Jie; Yang, Yanwu; Qin, Rui; Zeng, Daniel; Li, Xin
2011-12-03
Conference NameThe 21th Workshop on Information Technologies and Systems
Source PublicationProceedings of the 21th Workshop on Information Technologies and Systems
Conference DateDec. 3-4, 2011
Conference PlaceShanghai, China
AbstractWith serious advertising budget constraints, advertisers have to adjust their daily budget according to the performance of advertisements in real time. Thus we can leave precious budgets to better opportunities in the future, and avoid the surge of ineffective clicks for unnecessary costs. However, advertisers usually have no sufficient knowledge and time for real-time advertising operations in search auctions. We formulate the budget adjustment problem as a state-action decision process in the reinforcement learning (RL) framework. Considering dynamics of marketing environments and some distinctive features of search auctions, we extend continuous reinforcement learning to fit the budget decision scenarios. The market utility is defined as discounted total clicks to get during the remaining period of an advertising schedule. We conduct experiments to validate and evaluate our strategy of budget adjustment with real world data from search advertising campaigns. Experimental results showed that our strategy outperforms the two other baseline strategies.
KeywordSearch Auctions Budget Adjustment Reinforcement Learning Dynamical Adjustment
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19637
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
AffiliationThe State Key Lab of Intelligent Control and Management of Complex Systems, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Jie,Yang, Yanwu,Qin, Rui,et al. Dynamic Budget Adjustment in Search Auctions[C],2011.
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