Search (4 results, page 1 of 1)

  • × author_ss:"Watters, C."
  • × theme_ss:"Internet"
  • × year_i:[2000 TO 2010}
  1. Kellar, M.; Watters, C.; Shepherd, M.: ¬A field study characterizing Web-based information seeking tasks (2007) 0.00 
    4.6511332E-4 = product of:
  0.0069766995 = sum of:
    0.0069766995 = product of:
      0.013953399 = sum of:
        0.013953399 = weight(_text_:information in 335) [ClassicSimilarity], result of:
          0.013953399 = score(doc=335,freq=16.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.27429342 = fieldWeight in 335, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=335)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Previous studies have examined various aspects of user behavior on the Web, including general information-seeking patterns, search engine use, and revisitation habits. Little research has been conducted to study how users navigate and interact with their Web browser across different information-seeking tasks. We have conducted a field study of 21 participants, in which we logged detailed Web usage and asked participants to provide task categorizations of their Web usage based on the following categories: Fact Finding, Information Gathering, Browsing, and Transactions. We used implicit measures logged during each task session to provide usage measures such as dwell time, number of pages viewed, and the use of specific browser navigation mechanisms. We also report on differences in how participants interacted with their Web browser across the range of information-seeking tasks. Within each type of task, we found several distinguishing characteristics. In particular, Information Gathering tasks were the most complex; participants spent more time completing this task, viewed more pages, and used the Web browser functions most heavily during this task. The results of this analysis have been used to provide implications for future support of information seeking on the Web as well as direction for future research in this area.
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.7, S.999-1018
  2. Watters, C.; Wang, H.: Rating new documents for similarity (2000) 0.00 
    2.79068E-4 = product of:
  0.0041860198 = sum of:
    0.0041860198 = product of:
      0.0083720395 = sum of:
        0.0083720395 = weight(_text_:information in 4856) [ClassicSimilarity], result of:
          0.0083720395 = score(doc=4856,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.16457605 = fieldWeight in 4856, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=4856)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Electronic news has long held the promise of personalized and dynamic delivery of current event new items, particularly for Web users. Although wlwctronic versions of print news are now widely available, the personalization of that delivery has not yet been accomplished. In this paper, we present a methodology of associating news documents based on the extraction of feature phrases, where feature phrases identify dates, locations, people and organizations. A news representation is created from these feature phrases to define news objects that can then be compared and ranked to find related news items. Unlike tradtional information retrieval, we are much more interested in precision than recall. That is, the user would like to see one or more specifically related articles, rather than all somewhat related articles. The algorithm is designed to work interactively the the user using regular web browsers as the interface
    Source
    Journal of the American Society for Information Science. 51(2000) no.9, S.793-804
  3. Shepherd, M.; Duffy, J.F.J.; Watters, C.; Gugle, N.: ¬The role of user profiles for news filtering (2001) 0.00 
    2.3255666E-4 = product of:
  0.0034883497 = sum of:
    0.0034883497 = product of:
      0.0069766995 = sum of:
        0.0069766995 = weight(_text_:information in 5585) [ClassicSimilarity], result of:
          0.0069766995 = score(doc=5585,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.13714671 = fieldWeight in 5585, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5585)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Most on-line news sources are electronic versions of "ink-on-paper" newspapers. These are versions that have been filtered, from the mass of news produced each day, by an editorial board with a given community profile in mind. As readers, we choose the filter rather than choose the stories. New technology, however, provides the potential for personalized versions to be filtered automatically from this mass of news on the basis of user profiles. People read the news for many reasons: to find out "what's going on," to be knowledgeable members of a community, and because the activity itself is pleasurable. Given this, we ask the question, "How much filtering is acceptable to readers?" In this study, an evaluation of user preference for personal editions versus community editions of on-line news was performed. A personalized edition of a local newspaper was created for each subject based on an elliptical model that combined the user profile and community profile as represented by the full edition of the local newspaper. The amount of emphasis given the user profile and the community profile was varied to test the subjects' reactions to different amounts of personalized filtering. The task was simply, "read the news," rather than any subject specific information retrieval task. The results indicate that users prefer the coarse-grained community filters to fine-grained personalized filters
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.2, S.149-160
  4. Jordan, C.; Watters, C.: Addressing gaps in knowledge while reading (2009) 0.00 
    2.3255666E-4 = product of:
  0.0034883497 = sum of:
    0.0034883497 = product of:
      0.0069766995 = sum of:
        0.0069766995 = weight(_text_:information in 3158) [ClassicSimilarity], result of:
          0.0069766995 = score(doc=3158,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.13714671 = fieldWeight in 3158, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3158)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)
    Abstract
    Reading is a common everyday activity for most of us. In this article, we examine the potential for using Wikipedia to fill in the gaps in one's own knowledge that may be encountered while reading. If gaps are encountered frequently while reading, then this may detract from the reader's final understanding of the given document. Our goal is to increase access to explanatory text for readers by retrieving a single Wikipedia article that is related to a text passage that has been highlighted. This approach differs from traditional search methods where the users formulate search queries and review lists of possibly relevant results. This explicit search activity can be disruptive to reading. Our approach is to minimize the user interaction involved in finding related information by removing explicit query formulation and providing a single relevant result. To evaluate the feasibility of this approach, we first examined the effectiveness of three contextual algorithms for retrieval. To evaluate the effectiveness for readers, we then developed a functional prototype that uses the text of the abstract being read as context and retrieves a single relevant Wikipedia article in response to a passage the user has highlighted. We conducted a small user study where participants were allowed to use the prototype while reading abstracts. The results from this initial study indicate that users found the prototype easy to use and that using the prototype significantly improved their stated understanding and confidence in that understanding of the academic abstracts they read.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2255-2268

Authors

Themes