Advertisement

The Influence of Backstories on Queries with Varying Levels of Intent in Task-Based Specialised Information Retrieval

  • Manuel SteinerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

Various ways of determining ambiguity of search queries exist for general search. Relevancy of result documents for searches however is not determined by the query alone. The current user intent is what drives a search and determines if results are useful. Intent ambiguity describes queries that might have multiple intents. Conventional disambiguation methods might not work in specialised search where a goal is usually similar or the same (e.g. finding job offerings in job search). Research described in this document investigates how to determine single and possible multi-intent queries for job search and how contextual information, especially backstories, affect the job search process. Results will lead to a better understanding of how important backstories and the handling of intent ambiguity are in specialised information retrieval. The importance of test collections with built-in ambiguity to better test performance will also be indicated. The proposed research is conducted with data from a major Australian job search platform.

Keywords

Information need Backstory Intent ambiguity 

Notes

Acknowledgements

This research is partially supported by the Australian Research Council Project LP150100252 and SEEK Ltd.

References

  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM 2009, pp. 5–14 (2009)Google Scholar
  2. 2.
    Alonso, O., Mizzaro, S.: Using crowdsourcing for TREC relevance assessment. Inf. Process. Manag. 48(6), 1053–1066 (2012)CrossRefGoogle Scholar
  3. 3.
    Alonso, O., Rose, D.E., Stewart, B.: Crowdsourcing for relevance evaluation. SIGIR Forum 42(2), 9–15 (2008)CrossRefGoogle Scholar
  4. 4.
    Bailey, P., Moffat, A., Scholer, F., Thomas, P.: UQV100: a test collection with query variability. In: SIGIR 2016, pp. 725–728 (2016)Google Scholar
  5. 5.
    Borlund, P., Ingwersen, P.: The development of a method for the evaluation of interactive information retrieval systems. J. Doc. 53(3), 225–250 (1997)CrossRefGoogle Scholar
  6. 6.
    Borlund, P., Ingwersen, P.: The application of work tasks in connection with the evaluation of interactive information retrieval systems: empirical results. In: MIRA 1999 (1999)Google Scholar
  7. 7.
    Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998, pp. 335–336 (1998)Google Scholar
  8. 8.
    Chapelle, O., Ji, S., Liao, C., Velipasaoglu, E., Lai, L., Wu, S.: Intent-based diversification of web search results: metrics and algorithms. Inf. Retrieval 14(6), 572–592 (2011)CrossRefGoogle Scholar
  9. 9.
    Drosou, M., Pitoura, E.: Search result diversification. SIGMOD Rec. 39(1), 41–47 (2010)CrossRefGoogle Scholar
  10. 10.
    Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: WWW 2009, pp. 381–390 (2009)Google Scholar
  11. 11.
    Hafernik, C.T., Jansen, B.J.: Understanding the specificity of web search queries. In: CHI 2013, pp. 1827–1832 (2013)Google Scholar
  12. 12.
    Jones, K.S., Robertson, S.E., Sanderson, M.: Ambiguous requests: implications for retrieval tests, systems and theories. SIGIR Forum 41(2), 8–17 (2007)CrossRefGoogle Scholar
  13. 13.
    Krovetz, R., Croft, W.B.: Lexical ambiguity and information retrieval. ACM Trans. Inf. Syst. 10(2), 115–141 (1992)CrossRefGoogle Scholar
  14. 14.
    Mei, Q., Church, K.W.: Entropy of search logs: how hard is search? With personalization? With backoff? In: WSDM 2008, pp. 45–54 (2008)Google Scholar
  15. 15.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  16. 16.
    Phan, N., Bailey, P., Wilkinson, R.: Understanding the relationship of information need specificity to search query length. In: SIGIR 2007, pp. 709–710 (2007)Google Scholar
  17. 17.
    Radlinski, F., Dumais, S.T.: Improving personalized web search using result diversification. In: SIGIR 2006, pp. 691–692 (2006)Google Scholar
  18. 18.
    Sanderson, M.: Ambiguous queries: test collections need more sense. In: SIGIR 2008, pp. 499–506 (2008)Google Scholar
  19. 19.
    Wang, Y., Agichtein, E.: Query ambiguity revisited: clickthrough measures for distinguishing informational and ambiguous queries. In: NAACL 2010, pp. 361–364 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.RMIT UniversityMelbourneAustralia

Personalised recommendations