Search (13 results, page 1 of 1)

0.02704115 = product of:
  0.06760287 = sum of:
    0.023397226 = weight(_text_:retrieval in 6667) [ClassicSimilarity], result of:
      0.023397226 = score(doc=6667,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 6667, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6667)
    0.04420565 = weight(_text_:semantic in 6667) [ClassicSimilarity], result of:
      0.04420565 = score(doc=6667,freq=2.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.22969149 = fieldWeight in 6667, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6667)
  0.4 = coord(2/5)

Abstract

How knowledge is organized in human memory is of interest to both information science and cognitive science. The current information retrieval (IR) systems can be improved if we understand which conceptual structures could facilitate users in information processing and seeking. This project examined twenty-two cognitive maps on ten research topics generated by ten experts and eleven non-experts. Experts were those who had completed a research project on the topic prior to participating in this study, while non-experts were from the same academic department who were familiar with the topic but had not conducted any in-depth research on it. A research topic can be represented by a vocabulary and the relationships among the terms in the vocabulary. A cognitive map visualizes the vocabulary and its configuration in a plane. We observed that experts did not generate the maps much faster than non-experts. Both experts and non-experts modified the given vocabulary by either adding or dropping terms. The dominant configuration for the maps was top-down, while five maps were orientated in left-right or radical structure (from a center). Experts tended to use problem-oriented approach to organize the vocabulary while non-experts often applied discipline-oriented hierarchical structure. Despite of many differences in vocabulary and structure by individuals, there are terms clustered in a similar ways across maps indicating an agreed-upon semantic closeness among these terms

0.025699163 = product of:
  0.064247906 = sum of:
    0.023397226 = weight(_text_:retrieval in 6714) [ClassicSimilarity], result of:
      0.023397226 = score(doc=6714,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 6714, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6714)
    0.040850677 = product of:
      0.08170135 = sum of:
        0.08170135 = weight(_text_:web in 6714) [ClassicSimilarity], result of:
          0.08170135 = score(doc=6714,freq=18.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.5408555 = fieldWeight in 6714, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6714)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

In this paper, we report on the second part of an empirical study designed to observe how users interact with World Wide Web resources. Applying a holistic approach, the researches examine users' cognitive, affective, and physical behaviors during user-Web interaction in order to understand better the nature of information retrieval on the Web, the needs of Web users, and the problem-solving strategies Web users employ. From analyses and the participant verbalizations collected during monitored searches, the researchers developed a taxonomy of problem solving strategies. The coding scheme was developed based on a content analysis of the integrated process data. Information from triangulation follow-up with participants via anonymously completed questionnaires, the taxonomy, and analyses of search transcripts were collected to determine 1) what problems users encountered during the interaction and how users solved these problems; and 2) which problem-solving strategies Web users considered and selected for finding factual information. The focus of the coding was on the participants' cognitive, affective, and physical behaviors in response to the components of the problems encountered, which included problems of the following types: Web interfaces, users' mental models, and the Web information sources. Searching behavior and problem-solving patterns are described and interpreted within the relevant situational context and the problems users encountered are identified and analyzed. Both the problems users faced and their problem-solving approaches endeavored evidence a strong reliance on mental models of the features available on sites, the location of those features, and other interface design concepts

0.013102447 = product of:
  0.06551223 = sum of:
    0.06551223 = weight(_text_:retrieval in 543) [ClassicSimilarity], result of:
      0.06551223 = score(doc=543,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.46789268 = fieldWeight in 543, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.109375 = fieldNorm(doc=543)
  0.2 = coord(1/5)

Source

Interactive information seeking, behaviour and retrieval. Eds.: Ruthven, I. u. D. Kelly

0.010588376 = product of:
  0.052941877 = sum of:
    0.052941877 = weight(_text_:retrieval in 7960) [ClassicSimilarity], result of:
      0.052941877 = score(doc=7960,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.37811437 = fieldWeight in 7960, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=7960)
  0.2 = coord(1/5)

Abstract

Reports on part of a study of real users' behaviour in selecting documents from a list of citations resulting from a search of an information retrieval system. Document selection involves value judgements and decision making. Understanding how users evaluate documents and make decisions provides a basis for designing intelligent information retrieval system that can do a better job of predicting usefulness

0.0066177347 = product of:
  0.033088673 = sum of:
    0.033088673 = weight(_text_:retrieval in 2027) [ClassicSimilarity], result of:
      0.033088673 = score(doc=2027,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.23632148 = fieldWeight in 2027, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2027)
  0.2 = coord(1/5)

Abstract

This article presents part of phase 2 of a research project funded by the NSF-National Science Digital Library Project, which observed how academic users interact with the ScienceDirect information retrieval system for simulated class-related assignments. The ultimate goal of the project is twofold: (1) to find ways to improve science and engineering students' use of science e-journal systems; (2) to develop methods to measure user interaction behaviors. Process-tracing technique recorded participants' processes and interaction behaviors that are measurable; think-aloud protocol captured participants' affective and cognitive verbalizations; pre- and post-search questionnaires solicited demographic information, prior experience with the system, and comments. We explored possible relationships between affective feelings and cognitive behaviors. During search interactions both feelings and thoughts occurred frequently. Positive feelings were more common and were associated more often with thoughts about results. Negative feelings were associated more often with thoughts related to the system, search strategy, and task. Learning styles are also examined as a factor influencing behavior. Engineering graduate students with an assimilating learning style searched longer and paused less than those with a converging learning style. Further exploration of learning styles is suggested.

Footnote

Beitrag eines Themenbereichs: Evaluation of Interactive Information Retrieval Systems

0.006603842 = product of:
  0.03301921 = sum of:
    0.03301921 = product of:
      0.06603842 = sum of:
        0.06603842 = weight(_text_:web in 6561) [ClassicSimilarity], result of:
          0.06603842 = score(doc=6561,freq=6.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.43716836 = fieldWeight in 6561, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6561)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

This project analyzed 541,920 user queries submitted to and executed in an academic Website during a four-year period (May 1997 to May 2001) using a relational database. The purpose of the study is three-fold: (1) to understand Web users' query behavior; (2) to identify problems encountered by these Web users; (3) to develop appropriate techniques for optimization of query analysis and mining. The linguistic analyses focus an query structures, lexicon, and word associations using statistical measures such as Zipf distribution and mutual information. A data model with finest granularity is used for data storage and iterative analyses. Patterns and trends of querying behavior are identified and compared with previous studies.

0.0065361084 = product of:
  0.03268054 = sum of:
    0.03268054 = product of:
      0.06536108 = sum of:
        0.06536108 = weight(_text_:web in 423) [ClassicSimilarity], result of:
          0.06536108 = score(doc=423,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.43268442 = fieldWeight in 423, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.09375 = fieldNorm(doc=423)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

0.0056604366 = product of:
  0.028302183 = sum of:
    0.028302183 = product of:
      0.056604367 = sum of:
        0.056604367 = weight(_text_:web in 4341) [ClassicSimilarity], result of:
          0.056604367 = score(doc=4341,freq=6.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.37471575 = fieldWeight in 4341, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=4341)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

Eleven middle school children constructed hierarchical maps for two science categories selected from two Web directories, Yahooligans! and KidsClick! For each category, children constructed a pair of maps: one without links and one with links. Forty-tour maps were analyzed to identify similarities and differences. The structures of the maps were compared to the structures employed by the directories. Children were able to construct hierarchical maps and articulate the relationships among the concepts. At the global level (whole map), children's maps were not alike and did not match the structures of the Web directories. At the local levels (superordinate and subordinate), however, children shared similarities in the conceptual configurations, especially for the concrete concepts. For these concepts, substantial overlap was found between the children's structures and those employed in the directories. For the abstract concepts the configurations were diverse and did not match those in the directories. The findings of this study have impl!cations for design of systems that are more supportive of children's conceptual structures.

0.0056604366 = product of:
  0.028302183 = sum of:
    0.028302183 = product of:
      0.056604367 = sum of:
        0.056604367 = weight(_text_:web in 2796) [ClassicSimilarity], result of:
          0.056604367 = score(doc=2796,freq=6.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.37471575 = fieldWeight in 2796, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=2796)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

Session characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: hit and run sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from hit and run sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help.

0.004679445 = product of:
  0.023397226 = sum of:
    0.023397226 = weight(_text_:retrieval in 443) [ClassicSimilarity], result of:
      0.023397226 = score(doc=443,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 443, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=443)
  0.2 = coord(1/5)

Abstract

This article proposes a model of document selection by real users of a bibliographic retrieval system. It reports on Part 1 of a longitudinal study of decision making on document use by academics during a actual research project. (Part 2 followed up the same users on how the selected documents were actually used in subsequent stages). The participants are 25 self-selected faculty and graduate students in Agricultural Economics. After a reference interview, the researcher conducted a search of DIALOG databases and prepared a printout. The users selected documents from this printout, They were asked to read and think aloud while selecting documents. There verbal reports were recorded and analyzed from a utiliy-theoretic perspective. The following model of the decision-making in the selection process emerged: document information lemenets (DIEs) in document records provide the information for judging the documents on 11 criteria (including topicality, orientation, quality, novelty, and authority); the criteria judgments are comninded in an assessment of document value along 5 dimensions (Epistemic, functional, conditional, social, and emotional values), leading to the use decision. This model accounts for the use of personal knowledge and decision strategies applied in the selection process. The model has implications for the design of an intelligent document selection assistant

0.004679445 = product of:
  0.023397226 = sum of:
    0.023397226 = weight(_text_:retrieval in 3927) [ClassicSimilarity], result of:
      0.023397226 = score(doc=3927,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 3927, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3927)
  0.2 = coord(1/5)

Footnote

Beitrag in einem Special issue on biomedical information retrieval.

0.0027233788 = product of:
  0.013616893 = sum of:
    0.013616893 = product of:
      0.027233787 = sum of:
        0.027233787 = weight(_text_:web in 2376) [ClassicSimilarity], result of:
          0.027233787 = score(doc=2376,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.18028519 = fieldWeight in 2376, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2376)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

A multidimensional-scaling approach is used to analyze frequently used medical-topic terms in queries submitted to a Web-based consumer health information system. Based on a year-long transaction log file, five medical focus keywords (stomach, hip, stroke, depression, and cholesterol) and their co-occurring query terms are analyzed. An overlap-coefficient similarity measure and a conversion measure are used to calculate the proximity of terms to one another based on their co-occurrences in queries. The impact of the dimensionality of the visual configuration, the cutoff point of term co-occurrence for inclusion in the analysis, and the Minkowski metric power k on the stress value are discussed. A visual clustering of groups of terms based on the proximity within each focus-keyword group is also conducted. Term distributions within each visual configuration are characterized and are compared with formal medical vocabulary. This investigation reveals that there are significant differences between consumer health query-term usage and more formal medical terminology used by medical professionals when describing the same medical subject. Future directions are discussed.

0.0027233788 = product of:
  0.013616893 = sum of:
    0.013616893 = product of:
      0.027233787 = sum of:
        0.027233787 = weight(_text_:web in 5505) [ClassicSimilarity], result of:
          0.027233787 = score(doc=5505,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.18028519 = fieldWeight in 5505, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5505)
      0.5 = coord(1/2)
  0.2 = coord(1/5)

Abstract

Currently, web document repositories have been collaboratively created and edited. One of these repositories, Wikipedia, is facing an important problem: assessing the quality of Wikipedia. Existing approaches exploit techniques such as statistical models or machine leaning algorithms to assess Wikipedia article quality. However, existing models do not provide satisfactory results. Furthermore, these models fail to adopt a comprehensive feature framework. In this article, we conduct an extensive survey of previous studies and summarize a comprehensive feature framework, including text statistics, writing style, readability, article structure, network, and editing history. Selected state-of-the-art deep-learning models, including the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTMs) network, CNN-LSTMs, bidirectional LSTMs, and stacked LSTMs, are applied to assess the quality of Wikipedia. A detailed comparison of deep-learning models is conducted with regard to different aspects: classification performance and training performance. We include an importance analysis of different features and feature sets to determine which features or feature sets are most effective in distinguishing Wikipedia article quality. This extensive experiment validates the effectiveness of the proposed model.