Search (18 results, page 1 of 1)

  • × theme_ss:"Internet"
  • × theme_ss:"Suchtaktik"
  • × year_i:[2000 TO 2010}
  1. Drabenstott, K.M.: Web search strategies (2000) 0.01 
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    Date
    22. 9.1997 19:16:05
    Pages
    S.114-161
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. Ford, N.; Miller, D.; Moss, N.: Web search strategies and human individual differences : a combined analysis (2005) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.7, S.757-764
  12. Lucas, W.; Topi, H.: Form and function : the impact of query term and operator usage on Web search results (2002) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.2, S.95-108
  13. Hupfer, M.E.; Detlor, B.: Gender and Web information seeking : a self-concept orientation model (2006) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.8, S.1105-1115
  14. Jansen, B.J.; Resnick, M.: ¬An examination of searcher's perceptions of nonsponsored and sponsored links during ecommerce Web searching (2006) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.14, S.1949-1961
  15. Madden, A.D.; Ford, N.J.; Miller, D.; Levy, P.: Children's use of the internet for information-seeking : what strategies do they use, and what factors affect their performance? (2006) 0.00 
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    Source
    Journal of documentation. 62(2006) no.6, S.744-761
  16. Kellar, M.; Watters, C.; Shepherd, M.: ¬A field study characterizing Web-based information seeking tasks (2007) 0.00 
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    Journal of the American Society for Information Science and Technology. 58(2007) no.7, S.999-1018
  17. Mansourian, I.: Web search efficacy : definition and implementation (2008) 0.00 
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    Source
    Aslib proceedings. 60(2008) no.4, S.349-363
  18. Stacey, Alison; Stacey, Adrian: Effective information retrieval from the Internet : an advanced user's guide (2004) 0.00 
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    Pages
    325 S

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Themes