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.
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References
Borlund P and Ingwersen P (1997) The development of a method for the evaluation of interactive information retrieval systems. Journal of Documentation 53(3):225–250
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. 324–331
Borlund P (2003) The Concept of Relevance in IR. Journal of American Society for Information Science and Technology 54(10):913–925
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. 33–40
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. 107–124
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. 251–268
Chen H and Dhar V (1995) Cognitive process as a basis for intelligent retrieval systems design. Information Processing and Management 27(3):405–432
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):693–705
Cuadra CA and Katter RV (1967) Opening the black box of relevance. Journal of Documentation 23(4):251–303
Croft WB (1993) Knowledge-based and statistical approaches to text retrieval. IEEE Expert 8(2):8–12
Doyle LB (1963) Is relevance an adequate criterion in retrieval system evaluation? Proceedings of the American Documentation Institute. Chicago, 1963, pp. 199–200
Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Harlow, England. ISBN: 0-201-15767-5. p. 412
Harter SP (1992) Psychological relevance and information science. Journal of American Society for Information Science 43(9):602–615
Ingwersen P (1996) Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory. Journal of Documentation 52(1):3–50
Mauldin M, Carbonell J and Thomason R (1987) Beyond the keyword barrier: Knowledge-based information retrieval. Information Services and Use 7(4–5):103–117
Mizzaro S (1998) How many relevances in information retrieval? Interacting with Computers 10(3):305–322
Narendra KS and Thathachar MAL (1974) Learning automata: A survey. IEEE Transactions on Systems, Man, and Cybernetics, SMC-4(4):323–334
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. 122–125
Salton G and Buckley C (1998) Term weighting approaches in automatic text retrieval. Information Processing and Management 24(5):513–523
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. 381–399
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):321–343
Saracevic T (1996) Relevance reconsidered. In: Ingwersen P and Pors NO (eds). Information Science: Integration in Perspective. Copenhagen, pp. 201–218
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. 3–48
Schamber L, Eisenberg MB and Nilan MS (1990) A re-examination of relevance: Towards a dynamic, situational definition. Information Processing and Management 26(6):755–775
Sebastiani F (2002) Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1):1–47
Vakkari P and Hakala N (2000) Changes in relevance criteria and problem stages in task performance. Journal of Documentation 56(5):540–562
Voorhees EM and Harman D (2000) Overview of the Sixth Text Retrieval Conference (TREC-6). Information Processing and Management 36(1):3–35
Vrajitoru D (1998) Crossover improvement for the genetic algorithm in information retrieval. Information Processing and Management 34(4):405–415
Zhang J and Korfhage RR (1999) A distance and angle similarity measure method. Journal of the American Society for Information Science 50(9):772–778
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Nyongesa, H.O., Maleki-dizaji, S. User modelling using evolutionary interactive reinforcement learning. Inf Retrieval 9, 343–355 (2006). https://doi.org/10.1007/s10791-006-4536-3
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DOI: https://doi.org/10.1007/s10791-006-4536-3