Search (31 results, page 1 of 2)

  • × theme_ss:"Internet"
  • × theme_ss:"Suchtaktik"
  1. Hsieh-Yee, I.: Search tactics of Web users in searching for texts, graphics, known items and subjects : a search simulation study (1998) 0.02
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    Date
    25.12.1998 19:22:31
    Source
    Reference librarian. 1998, no.60, S.61-85
  2. Drabenstott, K.M.: Web search strategies (2000) 0.01
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    Date
    22. 9.1997 19:16:05
    Pages
    S.114-161
  3. Snow, B.: ¬The Internet's hidden content and how to find it (2000) 0.00
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    Footnote
    Vgl. auch: Notess, G.R.: Searching the hidden Internet. in: Database 20(1997) no.3, S.37-40.
    Source
    Online. 24(2000) no.3, S.61-66
  4. Colaric, S.M.: Instruction for Web searching : An empirical study (2003) 0.00
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    Source
    College and research libraries. 64(2003) no.2, S.111-122
  5. White, M.D.; Iivonen, M.: Questions as a factor in Web search strategy (2001) 0.00
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    Source
    Information processing and management. 37(2001) no.5, S.721-740
  6. Pharo, N.: Web information search strategies : a model for classifying Web interaction (1999) 0.00
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    Pages
    S.207-218
  7. Notess, G.R.: Searching the hidden Internet (1997) 0.00
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    Content
    Vgl. auch Snow, B.: The Internet's hidden content and how to find it. In: Online 24(2000) no.3, S.61-66.
    Source
    Database. 20(1997) no.3, S.37-40
  8. Kim, K.-S.; Allen, B.: Cognitive and task influences on Web searching behavior (2002) 0.00
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    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.2, S.109-119
  9. Pu, H.-T.; Chuang, S.-L.; Yang, C.: Subject categorization of query terms for exploring Web users' search interests (2002) 0.00
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    Abstract
    Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.
    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.8, S.617-630
  10. Kosmin, L.J.: Teaching Internet end-users effective search strategies across diversified databases (1992) 0.00
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    Pages
    S.225-231
  11. Makulowich, J.S.: 10 tips on managing your Internet searching (1995) 0.00
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    Source
    Online. 19(1995) no.4, S.32-37
  12. Ardito, S.C.: ¬The Internet : beginning or end of organized information? (1998) 0.00
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    Source
    Searcher. 6(1998) no.1, S.52-57
  13. Dennis, S.; Bruza, P.; McArthur, R.: Web searching : a process-oriented experimental study of three interactive search paradigms (2002) 0.00
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    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.2, S.120-133
  14. Spink, A.; Danby, S.; Mallan, K.; Butler, C.: Exploring young children's web searching and technoliteracy (2010) 0.00
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    Source
    Journal of documentation. 66(2010) no.2, S.191-206
  15. Steinhaus, I.: Online recherchieren : Ökonomische Wege zu Informationen (1997) 0.00
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    Pages
    282 S
  16. Cothey, V.: ¬A longitudinal study of World Wide Web users' information-searching behavior (2002) 0.00
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    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.2, S.67-78
  17. Hsieh-Yee, I.: Research on Web-search behavior (2001) 0.00
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    Source
    Library and information science research. 23(2001) no.2, S.167-185
  18. Ford, N.; Miller, D.; Moss, N.: Web search strategies and human individual differences : cognitive and demographic factors, Internet attitudes, and approaches (2005) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.7, S.741-756
  19. Ford, N.; Miller, D.; Moss, N.: Web search strategies and human individual differences : a combined analysis (2005) 0.00
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    Journal of the American Society for Information Science and Technology. 56(2005) no.7, S.757-764
  20. Sanchiza, M.; Chinb, J.; Chevaliera, A.; Fuc, W.T.; Amadieua, F.; Hed, J.: Searching for information on the web : impact of cognitive aging, prior domain knowledge and complexity of the search problems (2017) 0.00
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    Source
    Information processing and management. 53(2017) no.1, S.281-294