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Learning to Display in Sponsored Search

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Book cover Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8643))

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Abstract

In sponsored search, it is necessary for the search engine, to decide the right number of advertisements (ads) to display for each query, in the constraint of a limited commercial load. Because over displaying ads will lead to the commercial overload problem, driving some of the users away in the long run. Despite the importance of the issue, very few literatures have discussed about how to measure the commercial load in sponsored search. Thus it is difficult for the search engine to make decisions quantitatively in practice. As a primary study, we propose to quantify the commercial load by the average displayed ad number per query, and then we investigate the displaying strategy to optimize the total revenue, in the constraint of a limited commercial load. We formalize this task under the framework of the secretary problem. A novel dynamic algorithm is proposed, which is extended from the state-of-the-art multiple-choice secretary algorithm. Through theoretical analysis, we proof that our algorithm is approaching the optimal value; and through empirical analysis, we demonstrate that our algorithm outperforms the fundamental static algorithm significantly. The algorithm can scale up with respect to very large datasets.

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Notes

  1. 1.

    www.emarketer.com

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Acknowledgements

The work described in this paper was fully supported by National Natural Science Foundation of China (No. 61300076) and Ph.D. Programs Foundation of Ministry of Education of China (No. 20131101120035).

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Xin Xin & Heyan Huang

Authors
  1. Xin Xin
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  2. Heyan Huang
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Corresponding author

Correspondence to Xin Xin .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

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© 2014 Springer International Publishing Switzerland

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Xin, X., Huang, H. (2014). Learning to Display in Sponsored Search. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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