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Query ranking model for search engine query recommendation

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Abstract

In this paper, we propose a query ranking model to select and order queries for search engine query recommendations. In contrast to existing similarity-based query recommendation methods (Agglomerative clustering of a search engine query log, 2000; The query-flow graph: model and applications, 2008], this model is based on utility, and ranks a query based on the joint probability of events whereby a query is selected by the user, the search results of the query are selected by the user, and the chosen search results satisfy the user’s information needs. We thus define three utilities in our model: a query-level utility representing the attractiveness of a query to the user, a perceived utility measuring the user’s actions given the search results, and a posterior utility measuring the user’s satisfaction with the chosen search results. We propose methods to compute these three utilities from query log data. In experiments involving real query log data, our proposed query ranking model outperformed seven other baseline methods in generating useful recommendations.

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Notes

  1. In our experiments, we set μ β  = 4.

  2. In our experiments, we set μ γ  = 6.

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

  4. The Internal Revenue Service (IRS) is an organization of the United States federal government. The IRS is responsible for collecting taxes, and the interpretation and enforcement of the Internal Revenue Code.

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Acknowledgments

Joshua Zhexue Huang was supported by The National Natural Science Foundation of China under Grant No. 61473194.

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Authors and Affiliations

  1. Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, People’s Republic of China

    JianGuo Wang

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, People’s Republic of China

    JianGuo Wang

  3. College of Computer Science and Software Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen, 518060, People’s Republic of China

    Joshua Zhexue Huang

  4. Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing, 100190, People’s Republic of China

    Jiafeng Guo & Yanyan Lan

Authors
  1. JianGuo Wang
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  2. Joshua Zhexue Huang
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  3. Jiafeng Guo
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  4. Yanyan Lan
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Corresponding author

Correspondence to Joshua Zhexue Huang.

Additional information

This paper has been extended from our workshop paper [37].

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Wang, J., Huang, J.Z., Guo, J. et al. Query ranking model for search engine query recommendation. Int. J. Mach. Learn. & Cyber. 8, 1019–1038 (2017). https://doi.org/10.1007/s13042-015-0362-5

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