Search (13 results, page 1 of 1)

  • × year_i:[2020 TO 2030}
  1. 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) 5.78 
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  2. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 5.78 
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  3. Hammache, A.; Boughanem, M.: Term position-based language model for information retrieval (2021) 5.78 
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  4. Reimer, U.: Empfehlungssysteme (2023) 5.78 
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  5. 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) 5.78 
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  6. Purpura, A.; Silvello, G.; Susto, G.A.: Learning to rank from relevance judgments distributions (2022) 5.78 
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  7. Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023) 5.78 
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  8. Fuhr, N.: Modelle im Information Retrieval (2023) 5.78 
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  9. 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) 5.78 
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  10. Qi, Q.; Hessen, D.J.; Heijden, P.G.M. van der: Improving information retrieval through correspondenceanalysis instead of latent semantic analysis (2023) 5.78 
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  11. Moffat, A.; Mackenzie, J.: How much freedom does an effectiveness metric really have? (2024) 5.78 
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  12. Ghali, M.-K.; Farrag, A.; Won, D.; Jin, Y.: Enhancing knowledge retrieval with in-context learning and semantic search through Generative AI (2024) 5.78 
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  13. Krasakis, A.M.; Yates, A.; Kanoulas, E.: Corpus-informed Retrieval Augmented Generation of Clarifying Questions (2024) 5.78 
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