-
Pejtersen, A.M.: Investigation of search strategies in fiction based on an analysis of 134 user-librarian conversations (1979)
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- Source
- IRFIS 3. Ed: T. Henriksen
-
Hapke, T.: Recherchestrategien in elektronischen Datenbanken : Inhaltliche Elemente der Schulung von Informationskompetenz (nicht nur) an Universitätsbibliotheken (1999)
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-
Lu, K.; Joo, S.; Lee, T.; Hu, R.: Factors that influence query reformulations and search performance in health information retrieval : a multilevel modeling approach (2017)
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-
Mandala, R.; Tokunaga, T.; Tanaka, H.: Query expansion using heterogeneous thesauri (2000)
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Renugadevi, S.; Geetha, T.V.; Gayathiri, R.L.; Prathyusha, S.; Kaviya, T.: Collaborative search using an implicitly formed academic network (2014)
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- Date
- 20. 1.2015 18:30:22
-
Pharo, N.: Web information search strategies : a model for classifying Web interaction (1999)
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- Source
- Vocabulary as a central concept in digital libraries: interdisciplinary concepts, challenges, and opportunities : proceedings of the Third International Conference an Conceptions of Library and Information Science (COLIS3), Dubrovnik, Croatia, 23-26 May 1999. Ed. by T. Arpanac et al
-
Bense, H.: Finden ohne Suchen : automatische Benachrichtigungen über relevante wissenschaftliche Publikationen mit regelbasierter KI (2021)
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- Abstract
- Jeden Tag erscheint eine Flut neuer wissenschaftlichen Publikationen. Für Forscher ist es schwierig, dabei den Überblick zu behalten. Aktualität und Relevanz der Ergebnislisten von Suchmaschinen wie Google, scholar.google.com und wissenschaftlichen Suchportalen entsprechen oft nicht den Erwartungen der Forscher. Vorgestellt wird eine Methode, die als Finden ohne Suchen (FwS = finding without searching) bezeichnet wird. Diese Methode nutzt künstliche Intelligenz in Kombination mit ausdrucksstarken benutzerdefinierten Regeln für Benachrichtigungen über neue Publikationen über eine App.
-
Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017)
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- Abstract
- Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered.
-
Mayr, P.; Mutschke, P.; Petras, V.; Schaer, P.; Sure, Y.: Applying science models for search (2010)
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- Source
- Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
-
Vuong, T.; Saastamoinen, M.; Jacucci, G.; Ruotsalo, T.: Understanding user behavior in naturalistic information search tasks (2019)
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-
Whitmire, E.: Disciplinary differences and undergraduates' information-seeking behavior (2002)
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- Abstract
- This study applied the Biglan model of disciplinary differences to the information-seeking behavior patterns of 5,175 undergraduates responding to questions on the College Student Experiences Questionnaire (CSEQ). The Biglan model categorizes academic disciplines along three dimensions: (1) hard-soft, (2) pure-applied, and (3) life-nonlife systems. Using t-tests, this model proved to be valid for distinguishing differences in undergraduates' information-seeking behavior patterns among various academic disciplines. The results indicate that the Biglan model has implications for the redesign of academic library services and use as a valid theoretical framework for future library and information science research.
-
Byström, K.: Information seekers in context : an analysis of the 'doer' in INSU studies (1999)
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- Date
- 22. 3.2002 9:55:52
-
Morse, P.M.: Search theory and browsing (1970)
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- Date
- 22. 5.2005 19:53:09
-
Nicholas, D.; Huntington, P.; Williams, P.; Dobrowolski, T.: Re-appraising information seeking behaviour in a digital environment : bouncers, checkers, returnees and the like (2004)
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-
Morse, P.M.: Browsing and search theory (1973)
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- Date
- 22. 5.2005 19:52:29
-
Branch, J.L.: Investigating the information-seeking process of adolescents : the value of using think alouds and think afters (2000)
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- Source
- Library and information science research. 22(2000) no.4, S.371-382
-
Pu, H.-T.; Chuang, S.-L.; Yang, C.: Subject categorization of query terms for exploring Web users' search interests (2002)
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-
Hollink, V.; Tsikrika, T.; Vries, A.P. de: Semantic search log analysis : a method and a study on professional image search (2011)
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Kim, J.; Thomas, P.; Sankaranarayana, R.; Gedeon, T.; Yoon, H.-J.: Eye-tracking analysis of user behavior and performance in web search on large and small screens (2015)
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Karanam, S.; Oostendorp, H. van; Sanchiz, M.; Chin, J.; Fu, W.-T.: Cognitive modeling of age-related differences in information search behavior (2017)
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