Abstract
Experiments on the effectiveness of relevance feedback with real users are time-consuming and expensive. This makes simulation for rapid testing desirable. We define a user model, which helps to quantify some interaction decisions involved in simulated relevance feedback. First, the relevance criterion defines the relevance threshold of the user to accept documents as relevant to his/her needs. Second, the browsing effort refers to the patience of the user to browse through the initial list of retrieved documents in order to give feedback. Third, the feedback effort refers to the effort and ability of the user to collect feedback documents. We use the model to construct several simulated relevance feedback scenarios in a laboratory setting. Using TREC data providing graded relevance assessments, we study the effect of the quality and quantity of the feedback documents on the effectiveness of the relevance feedback and compare this to the pseudo-relevance feedback. Our results indicate that one can compensate large amounts of relevant but low quality feedback by small amounts of highly relevant feedback.
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References
Blair, D.C.: The Data-Document Distinction in Information Retrieval. Communications of the ACM 27(4), 369–374 (1984)
Dennis, S., McArthur, R., Bruza, P.D.: Searching the World Wide Web made easy? The cognitive load imposed by query refinement mechanisms. In: Proceedings of the 3rd Australian Document Computing Conference. Department of Computer Science, TR-518, pp. 65–71. University of Sydney, Sydney (1998)
Efthimiadis, E.N.: Query expansion. In: Williams, M.E. (ed.) Annual Review of Information Science and Technology (ARIST 31), vol. 31, pp. 121–187. Learned Information for the American Society for Information Science, Medford (1996)
Kekäläinen, J.: The effects of query complexity, expansion and structure on retrieval performance in probabilistic text retrieval. University of Tampere, Department of Information Studies, Ph.D. Thesis, Acta Universitatis Tamperensis 678, Tampere, Finland (1999), Available at http://www.info.uta.fi/tutkimus/fire/archive/QCES.pdf (Cited October 31, 2005)
Kekäläinen, J.: Binary and graded relevance in IR evaluations – Comparison of the effects on ranking of IR systems. Īnformation Processing & Management 41(5), 1019–1033 (2005)
Kekäläinen, J., Järvelin, K.: Using graded relevance assessments in IR evaluation. Journal of the American Society for Information Science and Technology 53(13), 1120–1129 (2002)
Pirkola, A., Leppänen, E., Järvelin, K.: The RATF Formula (Kwok’s Formula): Exploiting average term frequency in cross-language retrieval. Information Research, 7(2) (2002), http://InformationR.net/ir/7-2/infres72.html
Ruthven, I., Lalmas, M.: A survey on the use of relevance feedback for information access systems. Knowledge Engineering Review 18(2), 95–145 (2003)
Ruthven, I., Lalmas, M., van Rijsbergen, K.: Incorporating user search behaviour into relevance feedback. Journal of the American Society for Information Science and Technology 54(6), 529–549 (2003)
Salton, G.: Automatic Text Processing: The Transformation, Analysis And Retrieval of Information by Computer. Addison-Wesley, Reading (1989)
Sihvonen, A., Vakkari, P.: Subject knowledge, thesaurus-assisted query expansion and search success. In: RIAO, Coupling Approaches, Coupling Media And Coupling Languages for Information Retrieval, Proceedings of RIAO 2004 conference, pp. 393–404. C.I.D, Paris (2004)
Sormunen, E.: Liberal relevance criteria of TREC – Counting on negligible documents? In: Beaulieu, M., Baeza-Yates, R., Myaeng, S.H., Järvelin, K. (eds.) Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 25), Tampere, Finland, August 11-15, pp. 320–330. ACM Press, New York (2002)
Sormunen, E., Kekäläinen, J., Koivisto, J., Järvelin, K.: Document Text Characteristics Affect the Ranking of the Most Relevant Documents by Expanded Structured Queries. Journal of Documentation 57(3), 358–374 (2001)
Vakkari, P., Sormunen, E.: The Influence of Relevance Levels on the Effectiveness of Interactive Information Retrieval. Journal of the American Society for Information Science and Technology 55(11), 963–969 (2004)
Voorhees, E.: Evaluation by Highly Relevant Documents. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR 24), New Orleans, Lousiana, USA, September 9-13, pp. 74–82. ACM Press, New York (2001)
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Keskustalo, H., Järvelin, K., Pirkola, A. (2006). The Effects of Relevance Feedback Quality and Quantity in Interactive Relevance Feedback: A Simulation Based on User Modeling. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_18
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DOI: https://doi.org/10.1007/11735106_18
Publisher Name: Springer, Berlin, Heidelberg
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