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Learning to rank audience for behavioral targeting

Published: 19 July 2010 Publication History

Abstract

Behavioral Targeting (BT) is a recent trend of online advertising market. However, some classical BT solutions, which predefine the user segments for BT ads delivery, are sometimes too large to numerous long-tail advertisers, who cannot afford to buy any large user segments due to budget consideration. In this extend abstract, we propose to rank users according to their probability of interest in an advertisement in a learning to rank framework. We propose to extract three types of features between user behaviors such as search queries, ad click history etc and the ad content provided by advertisers. Through this way, a long-tail advertiser can select a certain number of top ranked users as needed from the user segments for ads delivery. In the experiments, we use a 30-days' ad click-through log from a commercial search engine. The results show that using our proposed features under a learning to rank framework, we can well rank users who potentially interest in an advertisement.

References

[1]
J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much behavioral targeting help online advertising? In Proceedings of the 18th International World Wide Web Conference (WWW), 2009.
[2]
R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, MA, MIT Press, 2000.

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      cover image ACM Conferences
      SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
      July 2010
      944 pages
      ISBN:9781450301534
      DOI:10.1145/1835449
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Publication History

      Published: 19 July 2010

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      Author Tags

      1. behavioral targeting
      2. learning to rank
      3. online advertising

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      SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      • (2017)User Query Enhancement for Behavioral TargetingOntologies and Big Data Considerations for Effective Intelligence10.4018/978-1-5225-2058-0.ch009(413-433)Online publication date: 2017
      • (2016)Learning to rankGenetic Programming and Evolvable Machines10.1007/s10710-016-9263-y17:3(203-230)Online publication date: 1-Sep-2016
      • (2013)Intent-Based User Segmentation with Query EnhancementInternational Journal of Information Retrieval Research10.4018/ijirr.20131001013:4(1-17)Online publication date: 1-Oct-2013
      • (2013)Behavioral Targeting Online AdvertisingData Mining10.4018/978-1-4666-2455-9.ch068(1320-1338)Online publication date: 2013
      • (2011)Behavioral Targeting Online AdvertisingOnline Multimedia Advertising10.4018/978-1-60960-189-8.ch012(213-232)Online publication date: 2011
      • (2011)Learning to rank audience for behavioral targeting in display adsProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063666(605-610)Online publication date: 24-Oct-2011
      • (2011)Relevant knowledge helps in choosing right teacherProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2009935(115-124)Online publication date: 24-Jul-2011

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