Search (2 results, page 1 of 1)

  • × author_ss:"Meyer, J."
  • × year_i:[2010 TO 2020}
  1. Nehmadi, L.; Meyer, J.; Parmet, Y.; Ben-Asher, N.: Predicting a screen area's perceived importance from spatial and physical attributes (2011) 0.00 
    0.0028703054 = product of:
  0.005740611 = sum of:
    0.005740611 = product of:
      0.011481222 = sum of:
        0.011481222 = weight(_text_:a in 4757) [ClassicSimilarity], result of:
          0.011481222 = score(doc=4757,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.2161963 = fieldWeight in 4757, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=4757)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The editor's decision where and how to place items on a screen is crucial for the design of information displays, such as websites. We developed a statistical model that can facilitate automating this process by predicting the perceived importance of screen items from their location and size. The model was developed based on a 2-step experiment in which we asked participants to rate the importance of text articles that differed in size, screen location, and title size. Articles were either presented for 0.5 seconds or for unlimited time. In a stepwise regression analysis, the model's variables accounted for 65% of the variance in the importance ratings. In a validation study, the model predicted 85% of the variance of the mean apparent importance of screen items. The model also predicted individual raters' importance perception ratings. We discuss the implications of such a model in the context of automating layout generation. An automated system for layout generation can optimize data presentation to suit users' individual information and display preferences.
    Type
    a
  2. Sela, M.; Lavie, T.; Inbar, O.; Oppenheim, I.; Meyer, J.: Personalizing news content : an experimental study (2015) 0.00 
    0.0014647468 = product of:
  0.0029294936 = sum of:
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = weight(_text_:a in 1604) [ClassicSimilarity], result of:
          0.005858987 = score(doc=1604,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11032722 = fieldWeight in 1604, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1604)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The delivery of personalized news content depends on the ability to predict user interests. We evaluated different methods for acquiring user profiles based on declared and actual interest in various news topics and items. In an experiment, 36 students rated their interest in six news topics and in specific news items and read on 6 days standard, nonpersonalized editions and personalized (basic or adaptive) news editions. We measured subjective satisfaction with the editions and expressed preferences, along with objective measures, to infer actual interest in items. Users' declared interest in news topics did not strongly predict their actual interest in specific news items. Satisfaction with all news editions was high, but participants preferred the personalized editions. User interest was weakly correlated with reading duration, article length, and reading order. Different measures predicted interest in different news topics. Explicit measures predicted interest in relatively clearly defined topics such as sports, but were less appropriate for broader topics such as science and technology. Our results indicate that explicit and implicit methods should be combined to generate user profiles. We suggest that a personalized newspaper should contain both general information and personalized items, selected based on specific combinations of measures for each of the different news topics. Based on the findings, we present a general model to decide on the personalization of news content to generate personalized editions for readers.
    Type
    a

Authors