Search (3 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Case Based Reasoning"
  1. He, W.; Tian, X.: ¬A longitudinal study of user queries and browsing requests in a case-based reasoning retrieval system (2017) 0.00
    0.0036430482 = product of:
      0.014572193 = sum of:
        0.014572193 = weight(_text_:information in 3600) [ClassicSimilarity], result of:
          0.014572193 = score(doc=3600,freq=12.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.23754507 = fieldWeight in 3600, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3600)
      0.25 = coord(1/4)
    
    Abstract
    This article reports on a longitudinal analysis of query logs of a web-based case library system during an 8-year period (from 2005 to 2012). The analysis studies 3 different information-seeking approaches: keyword searching, browsing, and case-based reasoning (CBR) searching provided by the system by examining the query logs that stretch over 8 years. The longitudinal dimension of this study offers unique possibilities to see how users used the 3 different approaches over time. Various user information-seeking patterns and trends are identified through the query usage pattern analysis and session analysis. The study identified different user groups and found that a majority of the users tend to stick to their favorite information-seeking approach to meet their immediate information needs and do not seem to care whether alternative search options will offer greater benefits. The study also found that return users used CBR searching much more frequently than 1-time users and tend to use more query terms to look for information than 1-time users.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1124-1136
  2. Mazzucchelli, A.; Sartori , F.: String similarity in CBR platforms : a preliminary study (2014) 0.00
    0.0020821756 = product of:
      0.008328702 = sum of:
        0.008328702 = weight(_text_:information in 1568) [ClassicSimilarity], result of:
          0.008328702 = score(doc=1568,freq=2.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.13576832 = fieldWeight in 1568, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1568)
      0.25 = coord(1/4)
    
    Series
    Communications in computer and information science; 478
  3. Akerele, O.; David, A.; Osofisan, A.: Using the concepts of Case Based Reasoning and Basic Categories for enhancing adaptation to the user's level of knowledge in Decision Support System (2014) 0.00
    0.0017847219 = product of:
      0.0071388874 = sum of:
        0.0071388874 = weight(_text_:information in 1449) [ClassicSimilarity], result of:
          0.0071388874 = score(doc=1449,freq=2.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.116372846 = fieldWeight in 1449, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=1449)
      0.25 = coord(1/4)
    
    Abstract
    In most search systems, mapping queries with documents employs techniques such as vector space model, naïve Bayes, Bayesian theorem etc. to classify resulting documents. In this research studies, we are proposing the use of the concept of basic categories to representing the user's level of knowledge based on the concepts he employed during his search activities, so that the system could propose adapted results based on the observed user's level of knowledge. Our hypothesis is that this approach will enhance the decision support system for solving decisional problems in which information retrieval constitutes the backbone technical problem.