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Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- 30. 3.2001 13:32:22
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Van der Veer Martens, B.; Fleet, C. van: Opening the black box of "relevance work" : a domain analysis (2012)
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Rijsbergen, C.J. van: ¬A fast hierarchic clustering algorithm (1970)
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Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- 25. 8.2005 17:42:22
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Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003)
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- 22. 3.2003 19:27:36
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Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023)
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Qi, Q.; Hessen, D.J.; Heijden, P.G.M. van der: Improving information retrieval through correspondenceanalysis instead of latent semantic analysis (2023)
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Sembok, T.M.T.; Rijsbergen, C.J. van: IMAGING: a relevant feedback retrieval with nearest neighbour clusters (1994)
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Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003)
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- Date
- 11. 9.2004 17:32:22
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Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003)
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- Abstract
- Ruthven, Mounia, and van Rijsbergen rank and select terms for query expansion using information gathered on searcher evaluation behavior. Using the TREC Financial Times and Los Angeles Times collections and search topics from TREC-6 placed in simulated work situations, six student subjects each preformed three searches on an experimental system and three on a control system with instructions to search by natural language expression in any way they found comfortable. Searching was analyzed for behavior differences between experimental and control situations, and for effectiveness and perceptions. In three experiments paired t-tests were the analysis tool with controls being a no relevance feedback system, a standard ranking for automatic expansion system, and a standard ranking for interactive expansion while the experimental systems based ranking upon user information on temporal relevance and partial relevance. Two further experiments compare using user behavior (number assessed relevant and similarity of relevant documents) to choose a query expansion technique against a non-selective technique and finally the effect of providing the user with knowledge of the process. When partial relevance data and time of assessment data are incorporated in term ranking more relevant documents were recovered in fewer iterations, however retrieval effectiveness overall was not improved. The subjects, none-the-less, rated the suggested terms as more useful and used them more heavily. Explanations of what the feedback techniques were doing led to higher use of the techniques.
-
Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001)
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Savoy, J.; Ndarugendamwo, M.; Vrajitoru, D.: Report on the TREC-4 experiment : combining probabilistic and vector-space schemes (1996)
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Belkin, N.J.; Cool, C.; Koenemann, J.; Ng, K.B.; Park, S.: Using relevance feedback and ranking in interactive searching (1996)
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Bar-Ilan, J.; Levene, M.: ¬The hw-rank : an h-index variant for ranking web pages (2015)
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Karlsson, A.; Hammarfelt, B.; Steinhauer, H.J.; Falkman, G.; Olson, N.; Nelhans, G.; Nolin, J.: Modeling uncertainty in bibliometrics and information retrieval : an information fusion approach (2015)
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Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007)
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