-
Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013)
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- Date
- 29. 6.2013 18:06:23
-
Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018)
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- Source
- 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
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Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
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- Abstract
- Purpose In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues. Design/methodology/approach This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods. Findings The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments. Originality/value Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.
- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
-
Ding, Y.: Topic-based PageRank on author cocitation networks (2011)
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- Date
- 17. 3.2011 18:08:23
-
Van der Veer Martens, B.; Fleet, C. van: Opening the black box of "relevance work" : a domain analysis (2012)
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- Date
- 11. 6.2012 14:23:00
-
Walz, J.: Analyse der Übertragbarkeit allgemeiner Rankingfaktoren von Web-Suchmaschinen auf Discovery-Systeme (2018)
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- Abstract
- Ziel: Ziel dieser Bachelorarbeit war es, die Übertragbarkeit der allgemeinen Rankingfaktoren, wie sie von Web-Suchmaschinen verwendet werden, auf Discovery-Systeme zu analysieren. Dadurch könnte das bisher hauptsächlich auf dem textuellen Abgleich zwischen Suchanfrage und Dokumenten basierende bibliothekarische Ranking verbessert werden. Methode: Hierfür wurden Faktoren aus den Gruppen Popularität, Aktualität, Lokalität, Technische Faktoren, sowie dem personalisierten Ranking diskutiert. Die entsprechenden Rankingfaktoren wurden nach ihrer Vorkommenshäufigkeit in der analysierten Literatur und der daraus abgeleiteten Wichtigkeit, ausgewählt. Ergebnis: Von den 23 untersuchten Rankingfaktoren sind 14 (61 %) direkt vom Ranking der Web-Suchmaschinen auf das Ranking der Discovery-Systeme übertragbar. Zu diesen zählen unter anderem das Klickverhalten, das Erstellungsdatum, der Nutzerstandort, sowie die Sprache. Sechs (26%) der untersuchten Faktoren sind dagegen nicht übertragbar (z.B. Aktualisierungsfrequenz und Ladegeschwindigkeit). Die Linktopologie, die Nutzungshäufigkeit, sowie die Aktualisierungsfrequenz sind mit entsprechenden Modifikationen übertragbar.
-
Dadashkarimia, J.; Shakery, A.; Failia, H.; Zamani, H.: ¬An expectation-maximization algorithm for query translation based on pseudo-relevant documents (2017)
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- Date
- 23. 1.2017 14:07:40
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
-
Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
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- Date
- 13. 9.2014 14:45:22
-
Wei, F.; Li, W.; Liu, S.: iRANK: a rank-learn-combine framework for unsupervised ensemble ranking (2010)
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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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Karisani, P.; Rahgozar, M.; Oroumchian, F.: Transforming LSA space dimensions into a rubric for an automatic assessment and feedback system (2016)
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-
Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012)
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- Date
- 11. 6.2012 14:22:34
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Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013)
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- Date
- 22. 3.2013 19:34:49
