Dynamic Budget Adjustment in Search Auctions

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Dynamic Budget Adjustment in Search Auctions
	Zhang, Jie; Yang, Yanwu; Qin, Rui; Zeng, Daniel; Li, Xin
	2011-12-03
会议名称	The 21th Workshop on Information Technologies and Systems
会议录名称	Proceedings of the 21th Workshop on Information Technologies and Systems
会议日期	Dec. 3-4, 2011
会议地点	Shanghai, China
摘要	With 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.
关键词	Search Auctions Budget Adjustment Reinforcement Learning Dynamical Adjustment
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19637
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	The State Key Lab of Intelligent Control and Management of Complex Systems, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Zhang, Jie,Yang, Yanwu,Qin, Rui,et al. Dynamic Budget Adjustment in Search Auctions[C],2011.

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