Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Dynamics of bid optimization in online advertisement auctions
Proceedings of the 16th international conference on World Wide Web
Estimating rates of rare events at multiple resolutions
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Budget constrained bidding in keyword auctions and online knapsack problems
Proceedings of the 17th international conference on World Wide Web
Online auctions and generalized secretary problems
ACM SIGecom Exchanges
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification of proxy labeled examples for marketing segment generation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic pricing with limited supply
Proceedings of the 13th ACM Conference on Electronic Commerce
Estimating conversion rate in display advertising from past erformance data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multimedia features for click prediction of new ads in display advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Bid optimizing and inventory scoring in targeted online advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Joint optimization of bid and budget allocation in sponsored search
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Online allocation of display ads with smooth delivery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Focused matrix factorization for audience selection in display advertising
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.