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Predicting click through rate for job listings

Published: 20 April 2009 Publication History

Abstract

Click Through Rate (CTR) is an important metric for ad systems, job portals, recommendation systems. CTR impacts publisher's revenue, advertiser's bid amounts in "pay for performance" business models. We learn regression models using features of the job, optional click history of job, features of "related" jobs. We show that our models predict CTR much better than predicting avg. CTR for all job listings, even in absence of the click history for the job listing.

References

[1]
M. Regelson, D. Fain. Predicting click-through rate using keyword clusters. Proceedings of Second Workshop on Sponsored Search Auctions, Jan. 2006.
[2]
Matthew Richardson and Ewa Dominowska and Robert Ragno. Predicting clicks: estimating the click-through rate for new ads. WWW '07, 521--530, 2007.
[3]
Treenet: http://www.salford-systems.com/Treenet.php
[4]
Yahoo! HotJobs home page http://hotjobs.yahoo.com
[5]
Ian H. Witten and Eibe Frank Data Mining: Practical machine learning tools and techniques, 2nd Edition, 2005

Cited By

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  • (2023)Deduction of efficient algorithm to anatomize job insights2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308375(1-6)Online publication date: 6-Jul-2023
  • (2021)Research on the Prediction of Advertisement Click-Through Rate Based on Logistic Regression ModelComputational and Experimental Simulations in Engineering10.1007/978-3-030-67090-0_38(473-481)Online publication date: 28-May-2021
  • (2019)Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis ApproachCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-030-30146-0_52(780-798)Online publication date: 18-Aug-2019
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  1. Predicting click through rate for job listings

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      cover image ACM Conferences
      WWW '09: Proceedings of the 18th international conference on World wide web
      April 2009
      1280 pages
      ISBN:9781605584874
      DOI:10.1145/1526709

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 20 April 2009

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      Author Tags

      1. CPC
      2. CTR
      3. GBDT
      4. click through rate
      5. gradient boosted decision trees
      6. jobs
      7. linear regression
      8. prediction
      9. treenet

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      View all
      • (2023)Deduction of efficient algorithm to anatomize job insights2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308375(1-6)Online publication date: 6-Jul-2023
      • (2021)Research on the Prediction of Advertisement Click-Through Rate Based on Logistic Regression ModelComputational and Experimental Simulations in Engineering10.1007/978-3-030-67090-0_38(473-481)Online publication date: 28-May-2021
      • (2019)Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis ApproachCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-030-30146-0_52(780-798)Online publication date: 18-Aug-2019
      • (2011)Learning to advertiseProceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II10.5555/2022850.2022892(506-518)Online publication date: 24-May-2011
      • (2011)Learning to Advertise: How Many Ads Are Enough?Advances in Knowledge Discovery and Data Mining10.1007/978-3-642-20847-8_42(506-518)Online publication date: 2011
      • (2010)Estimating advertisability of tail queries for sponsored searchProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835544(563-570)Online publication date: 19-Jul-2010

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