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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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- 15. 9.2023 12:28:29
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Fuhr, N.: Modelle im Information Retrieval (2023)
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- 24.11.2022 17:20:29
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Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022)
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- Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1140-1154
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Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020)
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- Journal of the Association for Information Science and Technology. 71(2020) no.3, S.349-369
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Pan, M.; Huang, J.X.; He, T.; Mao, Z.; Ying, Z.; Tu, X.: ¬A simple kernel co-occurrence-based enhancement for pseudo-relevance feedback (2020)
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- Journal of the Association for Information Science and Technology. 71(2020) no.3, S.264-281
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Hammache, A.; Boughanem, M.: Term position-based language model for information retrieval (2021)
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- Journal of the Association for Information Science and Technology. 72(2021) no.5, S.627-642
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Purpura, A.; Silvello, G.; Susto, G.A.: Learning to rank from relevance judgments distributions (2022)
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- Journal of the Association for Information Science and Technology. 73(2022) no.9, S.1236-1252
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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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- Journal of the Association for Information Science and Technology. 74(2023) no.8, S.1010-1025