Location-aware query reformulation for search engines

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

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    https://github.com/petewarden/geodict

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    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).

References

  1. 1.

    Baeza-Yates RA, Tiberi A (2007) Extracting semantic relations from query logs. In: KDD

  2. 2.

    Baeza-Yates RA, Hurtado CA, Mendoza M (2004) Query recommendation using query logs in search engines. In: Current trends in database technology - EDBT workshops

  3. 3.

    Bar-Yossef Z, Kraus N (2011) Context-sensitive query auto-completion. In: Proceedings of the 20th international conference on World wide web. ACM, pp 107–116

  4. 4.

    Berkhin P (2006) Bookmark-coloring algorithm for personalized pagerank computing. Internet Math 3:41–62

    Article  Google Scholar 

  5. 5.

    Boldi P, Bonchi F, Castillo C, Donato D, Gionis A, Vigna S (2008) The query-flow graph: model and applications. In: CIKM. ACM, pp 609–618

  6. 6.

    Bonchi F, Perego R, Silvestri F, Vahabi H, Venturini R (2012) Efficient query recommendations in the long tail via center-piece subgraphs. In: SIGIR. ACM, pp 345–354

  7. 7.

    Cadegnani S, Guerra F, Ilarri S, del Carmen M, Rodríguez-Hernández, Trillo-Lado R, Velegrakis Y, Amaro R (2017) Exploiting linguistic analysis on urls for recommending web pages: a comparative study. In: Transactions on computational collective intelligence XXVI. Springer, Berlin, pp 26–45

    Google Scholar 

  8. 8.

    Cai F, Liang S, de Rijke M (2014) Time-sensitive personalized query auto-completion. In: CIKM

  9. 9.

    Cao H, Jiang D, Pei J, He Q, Liao Z, Chen E, Li H (2008) Context-aware query suggestion by mining click-through and session data. In: KDD, pp 875–883

  10. 10.

    Chen Y-Y, Suel T, Markowetz A (2006) Efficient query processing in geographic web search engines. In: SIGMOD, pp 277–288

  11. 11.

    Craswell N, Szummer M (2007) Random walks on the click graph. In: SIGIR. ACM, pp 239–246

  12. 12.

    del Carmen M, Ilarri S, Trillo-Lado R, Hermoso R (2015) Location-aware recommendation systems: where we are and where we recommend to go. In: Proceedings of the workshop on location-aware recommendations, co-located with the 9th ACM conference on recommender systems (LocalRec-RecSys’ 15), pp 1–8

  13. 13.

    Downey D, Dumais S T, Horvitz E (2007) Heads and tails: studies of web search with common and rare queries. In: SIGIR

  14. 14.

    Duan H, Hsu B-J P (2011) Online spelling correction for query completion. In: Proceedings of the 20th international conference on World wide web. ACM, pp 117–126

  15. 15.

    Guo J, Cheng X, Xu G, Shen H (2010) A structured approach to query recommendation with social annotation data. In: CIKM. ACM, pp 619–628

  16. 16.

    Haveliwala T H (2002) Topic-sensitive pagerank. In: WWW. ACM, pp 517–526

  17. 17.

    Hsu B-J P, Ottaviano G (2013) Space-efficient data structures for top-k completion. In: Proceedings of the 22nd international conference on World Wide Web. ACM, pp 583–594

  18. 18.

    Hu S, Xiao C, Ishikawa Y (2018) An efficient algorithm for location-aware query autocompletion. IEICE Trans Inf Syst 101(1):181–192

    Article  Google Scholar 

  19. 19.

    Huang Z, Mamoulis N (2017) Location-aware query recommendation for search engines at scale. In: International symposium on spatial and temporal databases. Springer, Berlin, pp 203–220

    Google Scholar 

  20. 20.

    Huang Z, Cautis B, Cheng R, Zheng Y (2016) Kb-enabled query recommendation for long-tail queries. In: CIKM, pp 2107–2112

  21. 21.

    Lin C, Wang J, Lu J (2017) Location-sensitive query auto-completion. In: Proceedings of the 26th international conference on world wide web companion. International World Wide Web Conferences Steering Committee, pp 819–820

  22. 22.

    Myllymaki J, Singleton D, Cutter A, Lewis M, Eblen S Location based query suggestion, Oct. 30, 2012. US Patent 8,301,639

  23. 23.

    Ni X, Sun J-T, Chen Z, Mobile query suggestions with time-location awareness July 16 2013. US Patent 8

  24. 24.

    Ortega RE, Frederick R, Dorfman B Providing location-based auto-complete functionality, Aug. 10, 2010. US Patent 7,774,003

  25. 25.

    Qi S, Wu D, Mamoulis N (2016) Location aware keyword query suggestion based on document proximity. TKDE 28(1):82–97

    Google Scholar 

  26. 26.

    Shokouhi M (2013) Learning to personalize query auto-completion. In: SIGIR

  27. 27.

    Shokouhi M, Radinsky K (2012) Time-sensitive query auto-completion. In: SIGIR

  28. 28.

    Wen J-R, Nie J-Y, Zhang H-J (2001) Clustering user queries of a search engine. In: WWW

  29. 29.

    Yan X, Guo J, Cheng X (2011) Context-aware query recommendation by learning high-order relation in query logs. In: CIKM. ACM, pp 2073–2076

  30. 30.

    Zhang Z, Nasraoui O (2006) Mining search engine query logs for query recommendation. In: WWW, pp 1039–1040

  31. 31.

    Zhao Z, Song R, Xie X, He X, Zhuang Y (2015) Mobile query recommendation via tensor function learning. In: IJCAI, pp 4084–4090

  32. 32.

    Zheng Y, Bao Z, Shou L, Tung AK (2015) Inspire: a framework for incremental spatial prefix query relaxation. IEEE Trans Knowl Data Eng 27(7):1949–1963

    Article  Google Scholar 

Download references

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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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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Keywords

  • Query reformulation
  • Query recommendation
  • Query auto-completion
  • Spatial proximity
  • Spatial database