{"id":3073,"date":"2020-11-26T17:02:18","date_gmt":"2020-11-26T16:02:18","guid":{"rendered":"https:\/\/searchstudies.org\/?page_id=3073"},"modified":"2021-06-08T19:21:28","modified_gmt":"2021-06-08T17:21:28","slug":"relevance-clues","status":"publish","type":"page","link":"https:\/\/searchstudies.org\/de\/research\/relevance-clues\/","title":{"rendered":"Relevance Clues"},"content":{"rendered":"

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Relevanz-Hinweise: Entwicklung und empirische \u00dcberpr\u00fcfung eines Modells f\u00fcr Relevanzentscheidungen bei Suchergebnissen auf Basis individueller Nutzerkriterien<\/h2>\n<\/div>\n<\/div><\/div>
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In der heutigen wissensbasierten Gesellschaft suchen Menschen regelm\u00e4\u00dfig nach Informationen in digitalen Informationssystemen. Ob ein Suchergebnis f\u00fcr die L\u00f6sung ihrer Suchaufgabe hilfreich ist oder nicht, entscheiden sie auf der Grundlage unterschiedlicher und h\u00f6chst subjektiver Kriterien. Das Ziel dieses Forschungsprojekts ist es, subjektive Kriterien sowie die Interdependenz zwischen verschiedenen Kriterien auf individuelle Relevanzentscheidungen bez\u00fcglich Suchergebnissen in einem akademischen Kontext zu bestimmen. Verl\u00e4ssliche Ergebnisse werden durch die Anwendung eines experimentellen Forschungsdesigns mit menschlichen Probanden - ein Ansatz, der in der Informationswissenschaft noch selten verwendet wird - und durch den Aufbau einer ausreichend gro\u00dfen Stichprobe f\u00fcr statistisch gesicherte Aussagen erzielt.<\/span><\/p>\n

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Zu den bisherigen Projektergebnissen geh\u00f6rt ein Benutzermodell f\u00fcr Relevanzbewertungen in wissenschaftlichen Suchsystemen unter Ber\u00fccksichtigung von Popularit\u00e4tsdaten, z.B. Zitier- oder Downloadh\u00e4ufigkeiten der jeweiligen Arbeit. Das Modell zielt auf eine systematische Darstellung der Elemente eines Suchergebnisses als potenzielle Relevanzhinweise, der subjektiven Relevanzkriterien, die Nutzer bei der Bewertung der Ergebnisse anwenden, und der kontextuellen Relevanzfaktoren als Variablen, die die Relevanzurteile beeinflussen. Dar\u00fcber hinaus wurde ein methodischer Rahmen f\u00fcr die Einbeziehung von Popularit\u00e4tsdaten in experimentelle Studien zu Relevanzkriterien entwickelt.<\/p>\n

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Publikationen<\/h3>

Behnert, C. (2022): Popularit\u00e4t und Relevanz in der Suche<\/b>. Wiesbaden: Springer Vieweg. https:\/\/doi.org\/10.1007\/978-3-658-37512-6<\/a><\/p>\n

Behnert, C. (2019). Investigating the Effects of Popularity Data on Predictive Relevance Judgments in Academic Search Systems<\/b>. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval \u2013 CHIIR \u201919 (S. 437\u2013440). New York, New York, USA: ACM Press. https:\/\/doi.org\/10.1145\/3295750.3298978<\/a><\/p>\n

Behnert, C. (2019). Kriterien und Einflussfaktoren bei der Relevanzbewertung von Surrogaten in akademischen Informationssystemen<\/b>. In: Information \u2013 Wissenschaft & Praxis 70 (1), 24-32. https:\/\/doi.org\/10.1515\/iwp-2019-0002<\/a><\/p>\n

Behnert, C. (2017). Relevance Clues \u2013 Developing an Experimental Design to Examine the Criteria Behind Relevance Judgments<\/b>. In M. G\u00e4de, V. Trkulja, & V. Petras (Hrsg.), Everything Changes, Everything Stays the Same? Understanding Information Spaces. Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin, 13th\u201415th March 2017. Gl\u00fcckstadt: Verlag Werner H\u00fclsbusch.<\/p>\n\n <\/div><\/div><\/div>

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Projektteam<\/h3>

Christiane Behnert<\/a> (doctoral student)
\nUlrike Spree<\/a> (supervisor)<\/p>\n\n <\/div><\/div><\/div><\/div><\/div><\/section>

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