Search (50 results, page 1 of 3)

0.017311653 = product of:
  0.051934958 = sum of:
    0.03489179 = weight(_text_:b in 1821) [ClassicSimilarity], result of:
      0.03489179 = score(doc=1821,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 1821, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=1821)
    0.017043166 = product of:
      0.03408633 = sum of:
        0.03408633 = weight(_text_:22 in 1821) [ClassicSimilarity], result of:
          0.03408633 = score(doc=1821,freq=2.0), product of:
            0.1468348 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041930884 = queryNorm
            0.23214069 = fieldWeight in 1821, 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=1821)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Date

3. 5.1997 8:44:22

0.0144263785 = product of:
  0.043279134 = sum of:
    0.029076494 = weight(_text_:b in 2635) [ClassicSimilarity], result of:
      0.029076494 = score(doc=2635,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.19572285 = fieldWeight in 2635, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2635)
    0.014202639 = product of:
      0.028405279 = sum of:
        0.028405279 = weight(_text_:22 in 2635) [ClassicSimilarity], result of:
          0.028405279 = score(doc=2635,freq=2.0), product of:
            0.1468348 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041930884 = queryNorm
            0.19345059 = fieldWeight in 2635, 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=2635)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Date

22. 1.2016 11:25:47

0.0144263785 = product of:
  0.043279134 = sum of:
    0.029076494 = weight(_text_:b in 3343) [ClassicSimilarity], result of:
      0.029076494 = score(doc=3343,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.19572285 = fieldWeight in 3343, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3343)
    0.014202639 = product of:
      0.028405279 = sum of:
        0.028405279 = weight(_text_:22 in 3343) [ClassicSimilarity], result of:
          0.028405279 = score(doc=3343,freq=2.0), product of:
            0.1468348 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041930884 = queryNorm
            0.19345059 = fieldWeight in 3343, 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=3343)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

User-generated content is one of the most interesting phenomena of current published media, as users are now able not only to consume, but also to produce content in a much faster and easier manner. However, such freedom also carries concerns about content quality. In this work, we propose an automatic framework to assess the quality of collaboratively generated content. Quality is addressed as a multidimensional concept, modeled as a combination of independent assessments, each regarding different quality dimensions. Accordingly, we adopt a machine-learning (ML)-based multiview approach to assess content quality. We perform a thorough analysis of our framework on two different domains: Questions and Answer Forums and Collaborative Encyclopedias. This allowed us to better understand when and how the proposed multiview approach is able to provide accurate quality assessments. Our main contributions are: (a) a general ML multiview framework that takes advantage of different views of quality indicators; (b) the improvement (up to 30%) in quality assessment over the best state-of-the-art baseline methods; (c) a thorough feature and view analysis regarding impact, informativeness, and correlation, based on two distinct domains.

Date

16.11.2017 13:04:22

0.011336679 = product of:
  0.06802007 = sum of:
    0.06802007 = product of:
      0.13604014 = sum of:
        0.13604014 = weight(_text_:psychologie in 2588) [ClassicSimilarity], result of:
          0.13604014 = score(doc=2588,freq=4.0), product of:
            0.24666919 = queryWeight, product of:
              5.8827567 = idf(docFreq=334, maxDocs=44218)
              0.041930884 = queryNorm
            0.5515084 = fieldWeight in 2588, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.8827567 = idf(docFreq=334, maxDocs=44218)
              0.046875 = fieldNorm(doc=2588)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

BK

77.93 (Angewandte Psychologie)

Classification

77.93 (Angewandte Psychologie)

0.008393662 = product of:
  0.05036197 = sum of:
    0.05036197 = weight(_text_:b in 5292) [ClassicSimilarity], result of:
      0.05036197 = score(doc=5292,freq=6.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.33900195 = fieldWeight in 5292, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5292)
  0.16666667 = coord(1/6)

Abstract

The Internet has become the first source of consumer health information. Most theoretical and empirical studies are centered on information needs and seeking, rather than on information outcomes. This review's purpose is to explore and explain health outcomes of Online Consumer Health Information (OCHI) in primary care. A participatory systematic mixed studies review with a framework synthesis was undertaken. Starting from an initial conceptual framework, our specific objectives were to (a) identify types of OCHI outcomes in primary care, (b) identify factors associated with these outcomes, and (c) integrate these factors and outcomes into a comprehensive revised framework combining an information theory and a psychosocial theory of behavior. The results of 65 included studies were synthesized using a qualitative thematic data analysis. The themes derived from the literature underwent a harmonization process that produced a comprehensive typology of OCHI outcomes. The revised conceptual framework specifies four individual and one organizational level of OCHI outcomes, while including factors such as consumers' information needs and four interdependent contextual factors. It contributes to theoretical knowledge about OCHI health outcomes, and informs future research, information assessment methods, and tools to help consumers find and use health information.

0.008224075 = product of:
  0.049344447 = sum of:
    0.049344447 = weight(_text_:b in 1336) [ClassicSimilarity], result of:
      0.049344447 = score(doc=1336,freq=4.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.3321527 = fieldWeight in 1336, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=1336)
  0.16666667 = coord(1/6)

0.007753731 = product of:
  0.046522386 = sum of:
    0.046522386 = weight(_text_:b in 3506) [ClassicSimilarity], result of:
      0.046522386 = score(doc=3506,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.31315655 = fieldWeight in 3506, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0625 = fieldNorm(doc=3506)
  0.16666667 = coord(1/6)

0.007753731 = product of:
  0.046522386 = sum of:
    0.046522386 = weight(_text_:b in 4271) [ClassicSimilarity], result of:
      0.046522386 = score(doc=4271,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.31315655 = fieldWeight in 4271, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0625 = fieldNorm(doc=4271)
  0.16666667 = coord(1/6)

0.006853395 = product of:
  0.04112037 = sum of:
    0.04112037 = weight(_text_:b in 4450) [ClassicSimilarity], result of:
      0.04112037 = score(doc=4450,freq=4.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.2767939 = fieldWeight in 4450, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4450)
  0.16666667 = coord(1/6)

Abstract

Web data repositories usually contain references to thousands of real-world entities from multiple sources. It is not uncommon that multiple entities share the same label (polysemes) and that distinct label variations are associated with the same entity (synonyms), which frequently leads to ambiguous interpretations. Further, spelling variants, acronyms, abbreviated forms, and misspellings compound to worsen the problem. Solving this problem requires identifying which labels correspond to the same real-world entity, a process known as entity resolution. One approach to solve the entity resolution problem is to associate an authority identifier and a list of variant forms with each entity-a data structure known as an authority file. In this work, we propose a generic framework for implementing a method for generating authority files. Our method uses information from the Web to improve the quality of the authority file and, because of that, is referred to as WER-Web-based Entity Resolution. Our contribution here is threefold: (a) we discuss how to implement the WER framework, which is flexible and easy to adapt to new domains; (b) we run extended experimentation with our WER framework to show that it outperforms selected baselines; and (c) we compare the results of a specialized solution for author name resolution with those produced by the generic WER framework, and show that the WER results remain competitive.

0.006853395 = product of:
  0.04112037 = sum of:
    0.04112037 = weight(_text_:b in 252) [ClassicSimilarity], result of:
      0.04112037 = score(doc=252,freq=4.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.2767939 = fieldWeight in 252, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=252)
  0.16666667 = coord(1/6)

Abstract

The issue of how teens choose social networks and information communication technologies (ICT's) for personal communication is complex. This study focused on describing how U.S. teens from a highly technological suburban high school select ICT's for personal communication purposes. Two research questions guided the study: (a) What factors influence high school seniors' selection of online social networks and other ICT's for everyday communication? (b) How can social network theory (SNT) help to explain how teens select online social networks and other ICT's for everyday communication purposes? Using focus groups, a purposive sample of 45 teens were asked to discuss (a) their preferred methods for communicating with friends and family and why, (b) the reasons why they chose to engage (or not to engage) in online social networking, (c) how they selected ICT's for social networking and other communication purposes, and (d) how they decided whom to accept as online "friends." Findings indicated that many factors influenced participants' ICT selection practices including six major categories of selection factors: relationship factors, information/communication factors, social factors, systems factors, self-protection factors, and recipient factors. SNT was also helpful in explaining how "friendship" was a major determining factor in their communication media and platform choices.

0.006853395 = product of:
  0.04112037 = sum of:
    0.04112037 = weight(_text_:b in 255) [ClassicSimilarity], result of:
      0.04112037 = score(doc=255,freq=4.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.2767939 = fieldWeight in 255, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=255)
  0.16666667 = coord(1/6)

Abstract

Today, people perform many types of tasks on the web, including those that require multiple web sessions. In this article, we build on research about web tasks and present an in-depth evaluation of the types of tasks people perform on the web over multiple web sessions. Multisession web tasks are goal-based tasks that often contain subtasks requiring more than one web session to complete. We will detail the results of two longitudinal studies that we conducted to explore this topic. The first study was a weeklong web-diary study where participants self-reported information on their own multisession tasks. The second study was a monthlong field study where participants used a customized version of Firefox, which logged their interactions for both their own multisession tasks and their other web activity. The results from both studies found that people perform eight different types of multisession tasks, that these tasks often consist of several subtasks, that these lasted different lengths of time, and that users have unique strategies to help continue the tasks which involved a variety of web and browser tools such as search engines and bookmarks and external applications such as Notepad or Word. Using the results from these studies, we have suggested three guidelines for developers to consider when designing browser-tool features to help people perform these types of tasks: (a) to maintain a list of current multisession tasks, (b) to support multitasking, and (c) to manage task-related information between sessions.

0.006853395 = product of:
  0.04112037 = sum of:
    0.04112037 = weight(_text_:b in 5301) [ClassicSimilarity], result of:
      0.04112037 = score(doc=5301,freq=4.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.2767939 = fieldWeight in 5301, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5301)
  0.16666667 = coord(1/6)

Abstract

Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain-specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network-based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.

0.006784515 = product of:
  0.04070709 = sum of:
    0.04070709 = weight(_text_:b in 3770) [ClassicSimilarity], result of:
      0.04070709 = score(doc=3770,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.27401197 = fieldWeight in 3770, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3770)
  0.16666667 = coord(1/6)

Abstract

Die seinerzeit 19-jährige Studentin Katrin Scheibe war 2015 Teilnehmerin an einem Seminar der Uni Düsseldorf über "Social Live"-Streaming Services und hat mit anderen Studenten zusammen eine Live-Übertragung einer Sitzung über YouNow durchgeführt. Innerhalb des rund einstündigen Programms schnellte die Zuschauerzahl auf weit über 200 hoch. Die meist jugendlichen Zuseher empfanden es als höchst interessant, eine Uni-Lehrveranstaltung hautnah miterleben zu dürfen. Ebenso waren die Studenten von dem aktuellen und zeitnahen Thema begeistert und publizierten ihre Forschungsresultate unter einem Pseudonym (Mathilde B. Friedländer) in internationalen Fachzeitschriften.

0.0058152988 = product of:
  0.03489179 = sum of:
    0.03489179 = weight(_text_:b in 3433) [ClassicSimilarity], result of:
      0.03489179 = score(doc=3433,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 3433, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=3433)
  0.16666667 = coord(1/6)

Abstract

Despite the development of huge healthcare Web sites and powerful search engines, many searchers end their searches prematurely with incomplete information. Recent studies suggest that users often retrieve incomplete information because of the complex scatter of relevant facts about a topic across Web pages. However, little is understood about regularities underlying such information scatter. To probe regularities within the scatter of facts across Web pages, this article presents the results of two analyses: (a) a cluster analysis of Web pages that reveals the existence of three page clusters that vary in information density and (b) a content analysis that suggests the role each of the above-mentioned page clusters play in providing comprehensive information. These results provide implications for the design of Web sites, search tools, and training to help users find comprehensive information about a topic and for a hypothesis describing the underlying mechanisms causing the scatter. We conclude by briefly discussing how the analysis of information scatter, at the granularity of facts, complements existing theories of information-seeking behavior.

0.0058152988 = product of:
  0.03489179 = sum of:
    0.03489179 = weight(_text_:b in 4268) [ClassicSimilarity], result of:
      0.03489179 = score(doc=4268,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 4268, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=4268)
  0.16666667 = coord(1/6)

Abstract

Beschrieben wird die Entwicklung eines Systems, das auf Basis einer Point Of Interest-Datenbank eine automatische Freizeitplanung durchführt. Nach Auswahl eines Tagesthemas wie z. B. "Tag in der Natur" oder "Tag zu zweit", entwirft das System einen thematisch passenden Tagesplan mit mehreren Etappen in zeitlicher Abfolge. Zunächst wird das entwickelte System grob vorgestellt, gefolgt von einem Überblick über ähnliche aktuelle Forschungen. Im Anschluss folgen eine kurze Einführung in den Mashup-Begriff sowie eine Beschreibung der vom System genutzten Dienste. Schließlich werden die Komponenten des Systems erklärt und ein Ausblick in die Zukunft der POI-Dienste und -Suchmaschinen gegeben.

0.0058152988 = product of:
  0.03489179 = sum of:
    0.03489179 = weight(_text_:b in 4275) [ClassicSimilarity], result of:
      0.03489179 = score(doc=4275,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 4275, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=4275)
  0.16666667 = coord(1/6)

0.0058152988 = product of:
  0.03489179 = sum of:
    0.03489179 = weight(_text_:b in 1219) [ClassicSimilarity], result of:
      0.03489179 = score(doc=1219,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 1219, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=1219)
  0.16666667 = coord(1/6)

Abstract

Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total-Community-Purity and Total-Cluster-Purity both at approximately 0.8.

0.0058152988 = product of:
  0.03489179 = sum of:
    0.03489179 = weight(_text_:b in 3433) [ClassicSimilarity], result of:
      0.03489179 = score(doc=3433,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 3433, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=3433)
  0.16666667 = coord(1/6)

Abstract

This study investigates the interplay between online news, reader comments, and social networks to detect and characterize comments leading to the revelation of censored information. Censorship of identity occurs in different contexts-for example, the military censors the identity of personnel and the judiciary censors the identity of minors and victims. We address three objectives: (a) assess the relevance of identity censorship in the presence of user-generated comments, (b) understand the fashion of censorship circumvention (what people say and how), and (c) determine how comment analysis can aid in identifying decensorship and information leakage through comments. After examining 3,582 comments made on 48 articles containing obfuscated terms, we find that a systematic examination of comments can compromise identity censorship. We identify and categorize information leakage in comments indicative of knowledge of censored information that may result in information decensorship. We show that the majority of censored articles contained at least one comment leading to censorship circumvention.

0.0058152988 = product of:
  0.03489179 = sum of:
    0.03489179 = weight(_text_:b in 4288) [ClassicSimilarity], result of:
      0.03489179 = score(doc=4288,freq=2.0), product of:
        0.14855953 = queryWeight, product of:
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.041930884 = queryNorm
        0.23486741 = fieldWeight in 4288, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.542962 = idf(docFreq=3476, maxDocs=44218)
          0.046875 = fieldNorm(doc=4288)
  0.16666667 = coord(1/6)

0.005356151 = product of:
  0.032136906 = sum of:
    0.032136906 = product of:
      0.06427381 = sum of:
        0.06427381 = weight(_text_:22 in 4313) [ClassicSimilarity], result of:
          0.06427381 = score(doc=4313,freq=4.0), product of:
            0.1468348 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041930884 = queryNorm
            0.4377287 = fieldWeight in 4313, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4313)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Date

22. 6.2018 14:22:02