Search (2 results, page 1 of 1)

  • × author_ss:"Kim, M."
  • × type_ss:"a"
  1. Kim, M.; Baek, I.; Song, M.: Topic diffusion analysis of a weighted citation network in biomedical literature (2018) 0.01 
    0.0127291735 = product of:
  0.03818752 = sum of:
    0.03818752 = product of:
      0.114562556 = sum of:
        0.114562556 = weight(_text_:network in 4036) [ClassicSimilarity], result of:
          0.114562556 = score(doc=4036,freq=8.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.59043956 = fieldWeight in 4036, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=4036)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.
  2. Lim, J.; Kang, S.; Kim, M.: Automatic user preference learning for personalized electronic program guide applications (2007) 0.01 
    0.0063645868 = product of:
  0.01909376 = sum of:
    0.01909376 = product of:
      0.057281278 = sum of:
        0.057281278 = weight(_text_:network in 476) [ClassicSimilarity], result of:
          0.057281278 = score(doc=476,freq=2.0), product of:
            0.19402927 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.043569047 = queryNorm
            0.29521978 = fieldWeight in 476, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=476)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    In this article, we introduce a user preference model contained in the User Interaction Tools Clause of the MPEG-7 Multimedia Description Schemes, which is described by a UserPreferences description scheme (DS) and a UsageHistory description scheme (DS). Then we propose a user preference learning algorithm by using a Bayesian network to which weighted usage history data on multimedia consumption is taken as input. Our user preference learning algorithm adopts a dynamic learning method for learning real-time changes in a user's preferences from content consumption history data by weighting these choices in time. Finally, we address a user preference-based television program recommendation system on the basis of the user preference learning algorithm and show experimental results for a large set of realistic usage-history data of watched television programs. The experimental results suggest that our automatic user reference learning method is well suited for a personalized electronic program guide (EPG) application.

Authors

Years

Themes