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Location-aware query reformulation for search engines

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Abstract

Query reformulation, including query recommendation and query auto-completion, is a popular add-on feature of search engines, which provide related and helpful reformulations of a keyword query. Due to the dropping prices of smartphones and the increasing coverage and bandwidth of mobile networks, a large percentage of search engine queries are issued from mobile devices. This makes it possible to improve the quality of query recommendation and auto-completion by considering the physical locations of the query issuers. However, limited research has been done on location-aware query reformulation for search engines. In this paper, we propose an effective spatial proximity measure between a query issuer and a query with a location distribution obtained from its clicked URLs in the query history. Based on this, we extend popular query recommendation and auto-completion approaches to our location-aware setting, which suggest query reformulations that are semantically relevant to the original query and give results that are spatially close to the query issuer. In addition, we extend the bookmark coloring algorithm for graph proximity search to support our proposed query recommendation approaches online, and we adapt an A* search algorithm to support our query auto-completion approach. We also propose a spatial partitioning based approximation that accelerates the computation of our proposed spatial proximity. We conduct experiments using a real query log, which show that our proposed approaches significantly outperform previous work in terms of quality, and they can be efficiently applied online.

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Notes

  1. https://github.com/petewarden/geodict

  2. We do not further refine to get an exact result by looking into the locations within the cells, because we assume that those locations near the range r from the user are still spatially relevant (see the location in cell c6 of Fig. 4).

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Acknowledgments

We thank the reviewers for their valuable comments. This work is partially supported by GRF Grant 17205015 from Hong Kong Research Grant Council. It has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 657347.

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Authors and Affiliations

  1. The University of Hong Kong, Hong Kong, Hong Kong

    Zhipeng Huang & Yuqiu Qian

  2. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

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  1. Zhipeng Huang
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  2. Yuqiu Qian
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  3. Nikos Mamoulis
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Correspondence to Zhipeng Huang.

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Huang, Z., Qian, Y. & Mamoulis, N. Location-aware query reformulation for search engines. Geoinformatica 22, 869–893 (2018). https://doi.org/10.1007/s10707-018-0334-5

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