2011 BidLandscapeForecastinginOnline

• (Cui et al., 2011) ⇒ Ying Cui, Ruofei Zhang, Wei Li, and Jianchang Mao. (2011). “Bid Landscape Forecasting in Online Ad Exchange Marketplace.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020454

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Bid+Landscape+Forecasting+in+Online+Ad+Exchange+Marketplace
• http://dl.acm.org/citation.cfm?id=2020408.2020454&preflayout=flat#citedby

Quotes

Author Keywords

• Ad exchange marketplace; algorithms; bid landscape forecasting; data mining; experimentation; measurement; on-line information services; online display advertising; performance

Abstract

Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One important business model in online display advertising is Ad Exchange marketplace, also called non-guaranteed delivery (NGD), in which advertisers buy targeted page views and audiences on a spot market through real-time auction. In this paper, we describe a bid landscape forecasting system in NGD marketplace for any advertiser campaign specified by a variety of targeting attributes. In the system, the impressions that satisfy the campaign targeting attributes are partitioned into multiple mutually exclusive samples. Each sample is one unique combination of quantified attribute values. We develop a divide-and-conquer approach that breaks down the campaign-level forecasting problem. First, utilizing a novel star-tree data structure, we forecast the bid for each sample using non-linear regression by gradient boosting decision trees. Then we employ a mixture-of-log-normal model to generate campaign-level bid distribution based on the sample-level forecasted distributions. The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world's largest NGD advertising exchange system, outperforming two baseline methods in term of forecasting errors.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 BidLandscapeForecastinginOnline	Ruofei Zhang Ying Cui Jianchang Mao Wei Li			Bid Landscape Forecasting in Online Ad Exchange Marketplace				10.1145/2020408.2020454		2011