-
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)
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Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013)
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- Source
- B.I.T.online. 16(2013) H.3, S.190-200
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Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015)
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- Bibliothek: Forschung und Praxis. 39(2015) H.3, S.384-393
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Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018)
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- Perspektive Bibliothek. 7(2018) H.2, S.2-23
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Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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- Source
- Information - Wissenschaft und Praxis. 70(2019) H.1, S.14-23
-
Mayr, P.: Bradfordizing als Re-Ranking-Ansatz in Literaturinformationssystemen (2011)
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- Information - Wissenschaft und Praxis. 62(2011) H.1, S.3-10
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Mayr, P.: Bradfordizing mit Katalogdaten : Alternative Sicht auf Suchergebnisse und Publikationsquellen durch Re-Ranking (2010)
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- Source
- BuB. 62(2010) H.1, S.61-63
-
Oberhauser, O.: Relevance Ranking in den Online-Katalogen der "nächsten Generation" (2010)
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- Source
- Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 63(2010) H.1/2, S.25-37
-
Ding, Y.: Topic-based PageRank on author cocitation networks (2011)
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- Abstract
- Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
-
Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013)
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-
Tsai, C.-F.; Hu, Y.-H.; Chen, Z.-Y.: Factors affecting rocchio-based pseudorelevance feedback in image retrieval (2015)
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-
Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015)
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Liu, X.; Zheng, W.; Fang, H.: ¬An exploration of ranking models and feedback method for related entity finding (2013)
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Xu, B.; Lin, H.; Lin, Y.: Assessment of learning to rank methods for query expansion (2016)
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