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In targeted online advertising, advertisers look for maximizing campaignperformance under delivery constraint within budget schedule. Most of theadvertisers typically prefer to impose the delivery constraint to spend budgetsmoothly over the time in order to reach a wider range of audiences and have asustainable impact. Since lots of impressions are traded through publicauctions for online advertising today, the liquidity makes price elasticity andbid landscape between demand and supply change quite dynamically. Therefore, itis challenging to perform smooth pacing control and maximize campaignperformance simultaneously. In this paper, we propose a smart pacing approachin which the delivery pace of each campaign is learned from both offline andonline data to achieve smooth delivery and optimal performance goals. Theimplementation of the proposed approach in a real DSP system is also presented.Experimental evaluations on both real online ad campaigns and offlinesimulations show that our approach can effectively improve campaign performanceand achieve delivery goals.

Smart Pacing for Effective Online Ad Campaign Optimization