Information Retrieval Journal

, Volume 18, Issue 2, pp 145–165 | Cite as

A term-based methodology for query reformulation understanding

Article

Abstract

Key to any research involving session search is the understanding of how a user’s queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user’s information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user’s motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.

Keywords

Term model Click model Query reformulation 

References

  1. Cole, M. J., Gwizdka, J., Bierig, R., Belkin, N. J., Liu, J., Liu, C., & Zhang, X. (2010). Linking search tasks with low-level eye movement patterns. In Proceedings of the 28th Annual European Conference on Cognitive Ergonomics (ECCE ’10) (pp. 109–116). ACM.Google Scholar
  2. Cole, M. J., Gwizdka, J., Liu, C., Bierig, R., Belkin, N. J., & Zhang, X. (2011). Task and user effects on reading patterns in information search. Interacting with Computers, 23, 346–362.CrossRefGoogle Scholar
  3. Craswell, N., Zoeter, O., Taylor, M., & Ramsey, B. (2008). An experimental comparison of click position-bias models. In Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM ’08) (pp. 87–94). ACM.Google Scholar
  4. Granka, L. A., Joachims, T., & Gay, G. (2004). Eye-tracking analysis of user behavior in www search. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’04) (pp. 478–479). ACM.Google Scholar
  5. Guan, D., Zhang, S., & Yang, H. (2013). Utilizing query change for session search. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’13) (pp. 453–462). ACM.Google Scholar
  6. Huang, J., & Efthimiadis, E. N. (2009). Analyzing and evaluating query reformulation strategies in web search logs. In Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM ’09) (pp. 77–86). ACM.Google Scholar
  7. Jansen, B. J., Spink, A., & Pedersen, J. (2005). A temporal comparison of altavista web searching: Research articles. Journal of the American Society for Information Science and Technology, 56(6), 559–570.CrossRefGoogle Scholar
  8. Jansen, B. J., Booth, D. L., & Spink, A. (2009). Patterns of query reformulation during web searching. Journal of the American Society for Information Science and Technology, 60(7), 1358–1371.CrossRefGoogle Scholar
  9. Jiang, J., He, D., & Allan, J. (2014). Searching, browsing, and clicking in a search session: Changes in user behavior by task and over time. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’14) (pp. 607–616). ACM.Google Scholar
  10. Jin, X., Sloan, M., & Wang, J. (2013). Interactive exploratory search for multi page search results. In Proceedings of the 22nd International Conference on World Wide Web (WWW ’13), pp. 655–666.Google Scholar
  11. Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005). Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’05) (pp. 154–161). ACM.Google Scholar
  12. Kanoulas, E., Carterette, B., Hall, M., Clough, P., & Sanderson, M. (2011). Overview of the trec 2011 session track. In Proceedings of the 20th Text Retrieval Conference (TREC ’11).Google Scholar
  13. Kanoulas, E., Carterette, B., Hall, M., Clough, P., & Sanderson, M. (2012). Overview of the trec 2012 session track. In Proceedings of the 21st Text Retrieval Conference (TREC ’12).Google Scholar
  14. Kanoulas, E., Carterette, B., Hall, M., Clough, P., & Sanderson, M. (2013). Overview of the trec 2013 session track. In Proceedings of the 22nd Text Retrieval Conference (TREC ’13).Google Scholar
  15. Kim, Y., Hassan, A., White, R. W., & Zitouni, I. (2014). Modeling dwell time to predict click-level satisfaction. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM ’14) (pp. 193–202). ACM.Google Scholar
  16. Kinley, K., Tjondronegoro, D., Partridge, H., & Edwards, S. (2012). Human-computer interaction: The impact of users’ cognitive styles on query reformulation behaviour during web searching. In Proceedings of the 24th Australian Computer-Human Interaction Conference (OzCHI ’12) (pp. 299–307). ACM.Google Scholar
  17. Liu, C., Gwizdka, J., Liu, J., Xu, T., & Belkin, N. J. (2010). Analysis and evaluation of query reformulations in different task types. In Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem (ASIS&T ’10).Google Scholar
  18. Liu, J., & Belkin, N. J. (2010). Personalizing information retrieval for multi-session tasks: The roles of task stage and task type. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’10) (pp. 26–33). ACM.Google Scholar
  19. Liu, Y., Miao, J., Zhang, M., Ma, S., & Ru, L. (2011). How do users describe their information need: Query recommendation based on snippet click model. Expert Systems with Applications, 38(11), 13,847–13,856.Google Scholar
  20. Liu, Y., Wang, C., Zhou, K., Nie, J., Zhang, M., & Ma, S. (2014). From skimming to reading: A two-stage examination model for web search. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM ’14) (pp. 849–858). ACM.Google Scholar
  21. Marchionini, G. (2006). Exploratory search: From finding to understanding. Communications of the ACM, 49(4), 41–46.CrossRefGoogle Scholar
  22. Porter, M. F. (1997). An algorithm for suffix stripping. In K. S. Jones, P. Willet (Eds.), Readings in information retrieval (pp. 313–316). San Fransisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
  23. Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends in Information Retrieval, 3(4), 333–389.CrossRefGoogle Scholar
  24. Song, R., Luo, Z., Nie, J. Y., Yu, Y., & Hon, H. W. (2009). Identification of ambiguous queries in web search. Information Processing and Management, 45(2), 216–229.CrossRefGoogle Scholar
  25. Sparck Jones, K. (1988). A Statistical interpretation of term specificity and its application in retrieval. In P. Willett (Ed.), Document retrieval systems (pp. 132–142). London: Taylor Graham Publishing.Google Scholar
  26. White, R. W., & Drucker, S. M. (2007). Investigating behavioral variability in web search. In Proceedings of the 16th International Conference on World Wide Web (WWW ’07) (pp. 21–30). ACM.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.University College LondonLondonUK
  2. 2.Georgetown UniversityWashingtonUSA

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