Search (323 results, page 16 of 17)

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  1. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 5.78 
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  2. Ye, Z.; Huang, J.X.: ¬A learning to rank approach for quality-aware pseudo-relevance feedback (2016) 5.78 
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  3. Xu, B.; Lin, H.; Lin, Y.: Assessment of learning to rank methods for query expansion (2016) 5.78 
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  4. Karisani, P.; Rahgozar, M.; Oroumchian, F.: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system (2016) 5.78 
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  5. Jiang, X.; Sun, X.; Yang, Z.; Zhuge, H.; Lapshinova-Koltunski, E.; Yao, J.: Exploiting heterogeneous scientific literature networks to combat ranking bias : evidence from the computational linguistics area (2016) 5.78 
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  6. Dadashkarimia, J.; Shakery, A.; Failia, H.; Zamani, H.: ¬An expectation-maximization algorithm for query translation based on pseudo-relevant documents (2017) 5.78 
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  7. Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Xiangji Huang, J.; Ben Jemaa, M.: MF-Re-Rank : a modality feature-based re-ranking model for medical image retrieval (2018) 5.78 
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  8. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 5.78 
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  9. Abdelkareem, M.A.A.: In terms of publication index, what indicator is the best for researchers indexing, Google Scholar, Scopus, Clarivate or others? (2018) 5.78 
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  10. Li, H.; Wu, H.; Li, D.; Lin, S.; Su, Z.; Luo, X.: PSI: A probabilistic semantic interpretable framework for fine-grained image ranking (2018) 5.78 
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  11. Hubert, G.; Pitarch, Y.; Pinel-Sauvagnat, K.; Tournier, R.; Laporte, L.: TournaRank : when retrieval becomes document competition (2018) 5.78 
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  12. Jiang, J.-D.; Jiang, J.-Y.; Cheng, P.-J.: Cocluster hypothesis and ranking consistency for relevance ranking in web search (2019) 5.78 
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  13. Jacucci, G.; Barral, O.; Daee, P.; Wenzel, M.; Serim, B.; Ruotsalo, T.; Pluchino, P.; Freeman, J.; Gamberini, L.; Kaski, S.; Blankertz, B.: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval (2019) 5.78 
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  14. González-Ibáñez, R.; Esparza-Villamán, A.; Vargas-Godoy, J.C.; Shah, C.: ¬A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IR (2019) 5.78 
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  15. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 5.78 
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  16. 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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  17. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 5.78 
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  18. Hammache, A.; Boughanem, M.: Term position-based language model for information retrieval (2021) 5.78 
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  19. 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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  20. Purpura, A.; Silvello, G.; Susto, G.A.: Learning to rank from relevance judgments distributions (2022) 5.78 
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