Introducing Diversity to Log-Based Query Suggestions to Deal with Underspecified User Queries

  • Marcin Sydow
  • Krzysztof Ciesielski
  • Jakub Wajda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7053)


This paper presents novel approaches to deal with ambiguous or under-specified user queries in search engines. We propose two algorithms for automatic query suggestion that are based on query logs. Furthermore, we propose a novel approach of diversifying the suggestions in order to improve user experience and present a novel adaptation of the MMR diversification algorithm to this problem. We propose two novel query-similarity measures that are utilised by the algorithm. We also present promising preliminary experimental results that are conducted on real data.


Semantic Similarity User Query Preliminary Experimental Result Semantic Similarity Measure Query Suggestion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)Google Scholar
  2. 2.
    Anand, S.S., Mobasher, B.: Intelligent Techniques for Web Personalization. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 1–36. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM, New York (1998)Google Scholar
  4. 4.
    Clarke, C.L.A., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: SIGIR, pp. 659–666 (2008)Google Scholar
  5. 5.
    Paul, C., et al.: Multiple approaches to analysing query diversity. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 734–735. ACM (2009)Google Scholar
  6. 6.
    Goffman, W.: A searching procedure for information retrieval. Information Storage and Retrieval 2(2), 73–78 (1964)CrossRefzbMATHGoogle Scholar
  7. 7.
    Levenshtein, V.: Binary Codes for Correcting Deletions, Insertions, and Reversals. Doklady Akademii Nauk SSSR 163(4), 845–848 (1965)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Liu, F., Yu, C., Meng, W.: Personalized web search for improving retrieval effectiveness. IEEE Transactions on Knowledge and Data Engineering 16, 28–40 (2004)CrossRefGoogle Scholar
  9. 9.
    Pirrò, G., Seco, N.: Design, implementation and evaluation of a new semantic similarity metric combining features and intrinsic information content. In: Chung, S. (ed.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1271–1288. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Piskorski, J., Sydow, M.: String Distance Metrics for Reference Matching and Search Query Correction. In: Abramowicz, W. (ed.) BIS 2007. LNCS, vol. 4439, pp. 353–365. Springer, Heidelberg (2007), doi:10.1007/978-3-540-72035-5-27CrossRefGoogle Scholar
  11. 11.
    Piskorski, J., Sydow, M., Wieloch, K.: Comparison of string distance metrics for lemmatisation of named entities in polish. pp. 413–427 (2009)Google Scholar
  12. 12.
    Piskorski, J., Wieloch, K., Sydow, M.: On knowledge-poor methods for person name matching and lemmatization for highly inflectional languages. Information Retrieval 12(3), 275–299 (2009)CrossRefGoogle Scholar
  13. 13.
    Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: Proc. of the 29th Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 691–692. ACM, NY (2006)Google Scholar
  14. 14.
    Santos, R.L.T., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 881–890. ACM, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcin Sydow
    • 1
    • 2
  • Krzysztof Ciesielski
    • 1
  • Jakub Wajda
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  2. 2.Polish-Japanese Institute of Information TechnologyWarsawPoland

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