Search (13 results, page 1 of 1)

Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018) 0.01

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Content: Vgl.: https://journals.ub.uni-heidelberg.de/index.php/bibliothek/article/view/57797. Vgl. auch: URN (PDF): http://nbn-resolving.de/urn:nbn:de:bsz:16-pb-577977.
Source: Perspektive Bibliothek. 7(2018) H.2, S.2-23

Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015) 0.01

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Source: Bibliothek: Forschung und Praxis. 39(2015) H.3, S.384-393

Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015) 0.00
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Abstract

Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.

Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013) 0.00

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Date: 29. 6.2013 18:06:23

Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.00

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Date: 22. 8.2014 17:05:18

Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.00

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Date: 20. 1.2015 18:30:22
18. 9.2018 18:22:56

Mayr, P.: Bradfordizing als Re-Ranking-Ansatz in Literaturinformationssystemen (2011) 0.00

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Date: 9. 2.2011 17:47:29

Costa Carvalho, A. da; Rossi, C.; Moura, E.S. de; Silva, A.S. da; Fernandes, D.: LePrEF: Learn to precompute evidence fusion for efficient query evaluation (2012) 0.00

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Date: 24. 6.2012 14:29:10

Silva, R.M.; Gonçalves, M.A.; Veloso, A.: ¬A Two-stage active learning method for learning to rank (2014) 0.00

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Date: 26. 1.2014 20:29:57

Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.00

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Date: 29. 9.2018 12:32:59

Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.00

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Date: 11. 6.2012 14:22:34

Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013) 0.00

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Date: 22. 3.2013 19:34:49

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) 0.00

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Date: 29. 9.2018 11:43:31

Search (13 results, page 1 of 1)

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