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Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003)
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Losee, R.M.; Church Jr., L.: Are two document clusters better than one? : the cluster performance question for information retrieval (2005)
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Weller, K.; Stock, W.G.: Transitive meronymy : automatic concept-based query expansion using weighted transitive part-whole relations (2008)
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Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018)
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Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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Reimer, U.: Empfehlungssysteme (2023)
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Bilal, D.: Ranking, relevance judgment, and precision of information retrieval on children's queries : evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids (2012)
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- Abstract
- This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.
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Gauch, S.; Smith, J.B.: ¬An expert system for automatic query reformation (1993)
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Stock, M.; Stock, W.G.: Internet-Suchwerkzeuge im Vergleich (IV) : Relevance Ranking nach "Popularität" von Webseiten: Google (2001)
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Bodoff, D.; Wong, S.P.-S.: Documents and queries as random variables : history and implications (2006)
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Thelwall, M.; Vaughan, L.: New versions of PageRank employing alternative Web document models (2004)
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Mayr, P.: Bradfordizing als Re-Ranking-Ansatz in Literaturinformationssystemen (2011)
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Mayr, P.: Bradfordizing mit Katalogdaten : Alternative Sicht auf Suchergebnisse und Publikationsquellen durch Re-Ranking (2010)
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Oberhauser, O.: Relevance Ranking in den Online-Katalogen der "nächsten Generation" (2010)
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Lanvent, A.: Licht im Daten Chaos (2004)
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Fuhr, N.: Theorie des Information Retrieval I : Modelle (2004)
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Fuhr, N.: Modelle im Information Retrieval (2023)
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