Advertisement

Information Retrieval

, Volume 9, Issue 3, pp 343–355 | Cite as

User modelling using evolutionary interactive reinforcement learning

  • H. O. NyongesaEmail author
  • S. Maleki-dizaji
Article

Abstract

As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining information that accurately matches user information needs. A number of factors affect information retrieval effectiveness (the accuracy of matching user information needs against the retrieved information). First, users often do not present search queries in the form that optimally represents their information need. Second, the measure of a document’s relevance is often highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative (subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall.

Keywords

User information needs modelling Interactive evolutionary learning Information relevance Adaptive information retrieval 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Borlund P and Ingwersen P (1997) The development of a method for the evaluation of interactive information retrieval systems. Journal of Documentation 53(3):225–250CrossRefGoogle Scholar
  2. Borlund P and Ingwersen P (1998) Measures of relative relevance and ranked half-life: Performance indicators for interactive IR. In: Croft BW, Moffat A, van Rijsbergen C J, Wilkinson R and Zobel J (eds.). Proceedings of the 21st ACM Sigir Conference on Research and Development of Information Retrieval. Melbourne, 1998. ACM Press/York Press: Australia. pp. 324–331Google Scholar
  3. Borlund P (2003) The Concept of Relevance in IR. Journal of American Society for Information Science and Technology 54(10):913–925CrossRefGoogle Scholar
  4. Buckley C and Voorhees EM (2000) Evaluating evaluation measure stability. In: Voorhees EM and Harman D (eds.). Proceedings of the ACM Sigir Conference on Research and Development in Information Retrieval. Athens, ACM Press, New York, pp. 33–40Google Scholar
  5. Buckley C, Mitra M, Walz and Cardie C (1998) Using clustering and superconcepts within SMART: TREC-6. In Voorhees EM and Harman D (eds.). Proceeding of the sixth text retrieval conference (TREC-6). NIST Publication 500–240, pp. 107–124Google Scholar
  6. Campbell I and van Rijsbergen K (1996) The ostensive model of developing information needs. In: Ingwersen P and Pors NO (eds). Proceedings of the International Conference on Conceptions of Library and Information Science, CoLIS 2. Copenhagen, pp. 251–268Google Scholar
  7. Chen H and Dhar V (1995) Cognitive process as a basis for intelligent retrieval systems design. Information Processing and Management 27(3):405–432Google Scholar
  8. Chen H, Shankaranarayanan G, Iyer A and She L (1998) A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms and simulated annealing. Journal of American Society for Information Science 49(8):693–705CrossRefGoogle Scholar
  9. Cuadra CA and Katter RV (1967) Opening the black box of relevance. Journal of Documentation 23(4):251–303CrossRefGoogle Scholar
  10. Croft WB (1993) Knowledge-based and statistical approaches to text retrieval. IEEE Expert 8(2):8–12CrossRefGoogle Scholar
  11. Doyle LB (1963) Is relevance an adequate criterion in retrieval system evaluation? Proceedings of the American Documentation Institute. Chicago, 1963, pp. 199–200Google Scholar
  12. Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Harlow, England. ISBN: 0-201-15767-5. p. 412zbMATHGoogle Scholar
  13. Harter SP (1992) Psychological relevance and information science. Journal of American Society for Information Science 43(9):602–615CrossRefGoogle Scholar
  14. Ingwersen P (1996) Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. Journal of Documentation 52(1):3–50Google Scholar
  15. Mauldin M, Carbonell J and Thomason R (1987) Beyond the keyword barrier: Knowledge-based information retrieval. Information Services and Use 7(4–5):103–117Google Scholar
  16. Mizzaro S (1998) How many relevances in information retrieval? Interacting with Computers 10(3):305–322CrossRefGoogle Scholar
  17. Narendra KS and Thathachar MAL (1974) Learning automata: A survey. IEEE Transactions on Systems, Man, and Cybernetics, SMC-4(4):323–334MathSciNetGoogle Scholar
  18. Petry FE, Buckles BP and Braphu D (1993) Fuzzy information retrieval using genetic algorithms and relevance feedback. In: Bonzi S (ed). Proceedings of the Sixth Annual Meeting of the American Society for Information Science, Columbus, Ohio, 1993, pp. 122–125Google Scholar
  19. Salton G and Buckley C (1998) Term weighting approaches in automatic text retrieval. Information Processing and Management 24(5):513–523CrossRefGoogle Scholar
  20. Salton G and McGill MJ (1997) The SMART and SIRE Experimental Retrieval System. In: Sparck Jones K and Willett P (eds.). Readings in Information Retrieval. Morgan Kaufmann, San Francisco. ISBN: 1-558-60454-5. pp. 381–399Google Scholar
  21. Saracevic T (1975) Relevance: A review of and framework for the thinking on the notion in Information Science. Journal of American Society for Information Science 26(6):321–343Google Scholar
  22. Saracevic T (1996) Relevance reconsidered. In: Ingwersen P and Pors NO (eds). Information Science: Integration in Perspective. Copenhagen, pp. 201–218Google Scholar
  23. Schamber L (1994) Relevance and information behaviour. In: Williams ME (ed). Annual review of information science and technology (ARIST). Learned Information inc., Medford, NJ, pp. 3–48Google Scholar
  24. Schamber L, Eisenberg MB and Nilan MS (1990) A re-examination of relevance: Towards a dynamic, situational definition. Information Processing and Management 26(6):755–775CrossRefGoogle Scholar
  25. Sebastiani F (2002) Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1):1–47CrossRefGoogle Scholar
  26. Vakkari P and Hakala N (2000) Changes in relevance criteria and problem stages in task performance. Journal of Documentation 56(5):540–562CrossRefGoogle Scholar
  27. Voorhees EM and Harman D (2000) Overview of the Sixth Text Retrieval Conference (TREC-6). Information Processing and Management 36(1):3–35CrossRefGoogle Scholar
  28. Vrajitoru D (1998) Crossover improvement for the genetic algorithm in information retrieval. Information Processing and Management 34(4):405–415CrossRefGoogle Scholar
  29. Zhang J and Korfhage RR (1999) A distance and angle similarity measure method. Journal of the American Society for Information Science 50(9):772–778CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of BotswanaGaboroneBotswana
  2. 2.School of Computing and Management SciencesSheffield Hallam UniversityUK

Personalised recommendations