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QRM: A Probabilistic Model for Search Engine Query Recommendation

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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

This paper proposes a query ranking model (QRM) for query recommendation to the Web users of a search engine. Given an initial query in a search session, a set of queries for the user to select as the next query are ranked based on the joint probability that the query is to be selected by the user and that the result of the query is to be clicked by the user, and that the clicked result will satisfy the user’s information requirement. We define three utilities to solve the model, including a query level utility and two document level utilities that are the perceived utility representing user’s action on the query result and the posterior utility representing user’s satisfaction on the search result. We present the methods to compute the three utilities from the query log data. Experiment results on real query log data have demonstrated that the proposed query ranking model outperformed six baseline methods in generating recommendation queries.

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Notes

  1. 1.

    http://www.google.com/

  2. 2.

    http://www.yahoo.com/

  3. 3.

    http://www.bing.com/

  4. 4.

    http://research.microsoft.com/users/nickcr/wscd09/

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Acknowledgment

This research is supported by Shenzhen New Industry Development Fund under Grant No.JC201005270342A.

Author information

Authors and Affiliations

  1. Shenzhen Key Laboratory of High Performance Data Mining, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China

    JianGuo Wang & Joshua Zhexue Huang

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055, China

    JianGuo Wang

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Joshua Zhexue Huang

Authors
  1. JianGuo Wang
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  2. Joshua Zhexue Huang
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Corresponding author

Correspondence to JianGuo Wang .

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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Cite this paper

Wang, J., Huang, J.Z. (2014). QRM: A Probabilistic Model for Search Engine Query Recommendation. 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_59

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_59

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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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