Search (4 results, page 1 of 1)

0.030214114 = product of:
  0.06042823 = sum of:
    0.06042823 = product of:
      0.12085646 = sum of:
        0.12085646 = weight(_text_:assessment in 3290) [ClassicSimilarity], result of:
          0.12085646 = score(doc=3290,freq=4.0), product of:
            0.2801951 = queryWeight, product of:
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.050750602 = queryNorm
            0.43132967 = fieldWeight in 3290, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3290)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The large volumes of medical information available on the web may provide answers for a wide range of users attempting to solve health-related problems. While experts generally utilize reliable resources for diagnosis search and professional development, novices utilize different (social) web resources to obtain information that helps them manage their health or the health of people who they care for. A diverse number of related search topics address clinical diagnosis, advice searching, information sharing, connecting with experts, etc. This paper focuses on the extent to which expertise can impact clinical query formulation, document relevance assessment and retrieval performance in the context of tailoring retrieval models and systems to experts vs. non-experts. The results show that medical domain expertise 1) plays an important role in the lexical representations of information needs; 2) significantly influences the perception of relevance even among users with similar levels of expertise and 3) reinforces the idea that a single ground truth does not exist, thereby leading to the variability of system rankings with respect to the level of user's expertise. The findings of this study presents opportunities for the design of personalized health-related IR systems, but also for providing insights about the evaluation of such systems.

0.021364605 = product of:
  0.04272921 = sum of:
    0.04272921 = product of:
      0.08545842 = sum of:
        0.08545842 = weight(_text_:assessment in 1501) [ClassicSimilarity], result of:
          0.08545842 = score(doc=1501,freq=2.0), product of:
            0.2801951 = queryWeight, product of:
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.050750602 = queryNorm
            0.30499613 = fieldWeight in 1501, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1501)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Recently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real-life applications. In this article, we present a new fuzzy-based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog,1 emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state-of-the-art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning-to-rank algorithms.

0.010314009 = product of:
  0.020628018 = sum of:
    0.020628018 = product of:
      0.041256037 = sum of:
        0.041256037 = weight(_text_:22 in 108) [ClassicSimilarity], result of:
          0.041256037 = score(doc=108,freq=2.0), product of:
            0.17771997 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050750602 = queryNorm
            0.23214069 = fieldWeight in 108, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=108)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

20. 4.2012 13:19:22

0.0085950075 = product of:
  0.017190015 = sum of:
    0.017190015 = product of:
      0.03438003 = sum of:
        0.03438003 = weight(_text_:22 in 664) [ClassicSimilarity], result of:
          0.03438003 = score(doc=664,freq=2.0), product of:
            0.17771997 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050750602 = queryNorm
            0.19345059 = fieldWeight in 664, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=664)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2013 19:34:49