Search (5 results, page 1 of 1)

0.066039264 = product of:
  0.13207853 = sum of:
    0.11198343 = weight(_text_:search in 108) [ClassicSimilarity], result of:
      0.11198343 = score(doc=108,freq=16.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.6516894 = fieldWeight in 108, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=108)
    0.02009509 = product of:
      0.04019018 = sum of:
        0.04019018 = weight(_text_:22 in 108) [ClassicSimilarity], result of:
          0.04019018 = score(doc=108,freq=2.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = 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(2/4)

Abstract

The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user's search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.

Date

20. 4.2012 13:19:22

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64434.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.043898437 = product of:
  0.087796874 = sum of:
    0.041137107 = weight(_text_:web in 3290) [ClassicSimilarity], result of:
      0.041137107 = score(doc=3290,freq=4.0), product of:
        0.16134618 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.049439456 = queryNorm
        0.25496176 = fieldWeight in 3290, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3290)
    0.046659768 = weight(_text_:search in 3290) [ClassicSimilarity], result of:
      0.046659768 = score(doc=3290,freq=4.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.27153727 = fieldWeight in 3290, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3290)
  0.5 = coord(2/4)

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.

0.008248359 = product of:
  0.032993436 = sum of:
    0.032993436 = weight(_text_:search in 1501) [ClassicSimilarity], result of:
      0.032993436 = score(doc=1501,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.19200584 = fieldWeight in 1501, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1501)
  0.25 = coord(1/4)

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.

0.008248359 = product of:
  0.032993436 = sum of:
    0.032993436 = weight(_text_:search in 2341) [ClassicSimilarity], result of:
      0.032993436 = score(doc=2341,freq=2.0), product of:
        0.17183559 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.049439456 = queryNorm
        0.19200584 = fieldWeight in 2341, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2341)
  0.25 = coord(1/4)

Abstract

A large body of research work examined, from both the query side and the user behavior side, the characteristics of medical- and health-related searches. One of the core issues in medical information retrieval (IR) is diversity of tasks that lead to diversity of categories of information needs and queries. From the evaluation perspective, another related and challenging issue is the limited availability of appropriate test collections allowing the experimental validation of medically task oriented IR techniques and systems. In this paper, we explore the peculiarities of TREC and CLEF medically oriented tasks and queries through the analysis of the differences and the similarities between queries across tasks, with respect to length, specificity, and clarity features and then study their effect on retrieval performance. We show that, even for expert oriented queries, language specificity level varies significantly across tasks as well as search difficulty. Additional findings highlight that query clarity factors are task dependent and that query terms specificity based on domain-specific terminology resources is not significantly linked to term rareness in the document collection. The lessons learned from our study could serve as starting points for the design of future task-based medical information retrieval frameworks.

0.0041864775 = product of:
  0.01674591 = sum of:
    0.01674591 = product of:
      0.03349182 = sum of:
        0.03349182 = weight(_text_:22 in 664) [ClassicSimilarity], result of:
          0.03349182 = score(doc=664,freq=2.0), product of:
            0.17312855 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049439456 = 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)
  0.25 = coord(1/4)

Date

22. 3.2013 19:34:49