Search (65 results, page 1 of 4)

0.032256197 = product of:
  0.048384294 = sum of:
    0.021338228 = weight(_text_:on in 1149) [ClassicSimilarity], result of:
      0.021338228 = score(doc=1149,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.19440265 = fieldWeight in 1149, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0625 = fieldNorm(doc=1149)
    0.027046064 = product of:
      0.054092128 = sum of:
        0.054092128 = weight(_text_:22 in 1149) [ClassicSimilarity], result of:
          0.054092128 = score(doc=1149,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.30952093 = fieldWeight in 1149, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1149)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.

Date

17.12.2013 11:02:22

0.032002483 = product of:
  0.048003722 = sum of:
    0.027719175 = weight(_text_:on in 108) [ClassicSimilarity], result of:
      0.027719175 = score(doc=108,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.25253648 = fieldWeight in 108, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=108)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 108) [ClassicSimilarity], result of:
          0.040569093 = score(doc=108,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

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

0.032002483 = product of:
  0.048003722 = sum of:
    0.027719175 = weight(_text_:on in 3246) [ClassicSimilarity], result of:
      0.027719175 = score(doc=3246,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.25253648 = fieldWeight in 3246, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=3246)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 3246) [ClassicSimilarity], result of:
          0.040569093 = score(doc=3246,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 3246, 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=3246)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose The purpose of this paper is to discuss the affective premises and economics of the influence of search engines on knowing and informing in the contemporary society. Design/methodology/approach A conceptual discussion of the affective premises and framings of the capitalist economics of knowing is presented. Findings The main proposition of this text is that the exploitation of affects is entwined in the competing market and emancipatory discourses and counter-discourses both as intentional interventions, and perhaps even more significantly, as unintentional influences that shape the ways of knowing in the peripheries of the regime that shape cultural constellations of their own. Affective capitalism bounds and frames our ways of knowing in ways that are difficult to anticipate and read even from the context of the regime itself. Originality/value In the relatively extensive discussion on the role of affects in the contemporary capitalism, influence of affects on knowing and their relation to search engine use has received little explicit attention so far.

Date

20. 1.2015 18:30:22

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 444) [ClassicSimilarity], result of:
      0.026672786 = score(doc=444,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 444, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=444)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 444) [ClassicSimilarity], result of:
          0.03380758 = score(doc=444,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 444, 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=444)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation.

Date

30. 9.2012 19:27:22

0.02905105 = product of:
  0.043576576 = sum of:
    0.026672786 = weight(_text_:on in 5288) [ClassicSimilarity], result of:
      0.026672786 = score(doc=5288,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 5288, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5288)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 5288) [ClassicSimilarity], result of:
          0.03380758 = score(doc=5288,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 5288, 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=5288)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google's top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.

Date

20. 1.2015 18:30:22

0.026668733 = product of:
  0.0400031 = sum of:
    0.02309931 = weight(_text_:on in 5493) [ClassicSimilarity], result of:
      0.02309931 = score(doc=5493,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.21044704 = fieldWeight in 5493, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5493)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 5493) [ClassicSimilarity], result of:
          0.03380758 = score(doc=5493,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 5493, 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=5493)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

Date

20. 1.2015 18:30:22

0.024192145 = product of:
  0.036288217 = sum of:
    0.016003672 = weight(_text_:on in 93) [ClassicSimilarity], result of:
      0.016003672 = score(doc=93,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 93, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=93)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 93) [ClassicSimilarity], result of:
          0.040569093 = score(doc=93,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 93, 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=93)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper is about search of photos in photo databases of agencies which sell photos over the Internet. The problem is far from the behavior of photo databases managed by librarians and also far from the corpora generally used for research purposes. The descriptions use mainly single words and it is well known that it is not the best way to have a good search. This increases the problem of semantic ambiguity. This problem of semantic ambiguity is crucial for cross-language querying. On the other hand, users are not aware of documentation techniques and use generally very simple queries but want to get precise answers. This paper gives the experience gained in a 3 year use (2006-2008) of a cross-language access to several of the main international commercial photo databases. The languages used were French, English, and German.

Date

17. 4.2012 14:25:22

0.024192145 = product of:
  0.036288217 = sum of:
    0.016003672 = weight(_text_:on in 105) [ClassicSimilarity], result of:
      0.016003672 = score(doc=105,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 105, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 105) [ClassicSimilarity], result of:
          0.040569093 = score(doc=105,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.23214069 = fieldWeight in 105, 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=105)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Result clustering has recently attracted a lot of attention to provide the users with a succinct overview of relevant search results than traditional search engines. This chapter proposes a mixed clustering method to organize all returned search results into a hierarchical tree structure. The clustering method accomplishes two main tasks, one is label construction and the other is tree building. This chapter uses precision to measure the quality of clustering results. According to the results of experiments, the author preliminarily concluded that the performance of the system is better than many other well-known commercial and academic systems. This chapter makes several contributions. First, it presents a high performance system based on the clustering method. Second, it develops a divisive hierarchical clustering algorithm to organize all returned snippets into hierarchical tree structure. Third, it performs a wide range of experimental analyses to show that almost all commercial systems are significantly better than most current academic systems.

Date

17. 4.2012 15:22:11

0.023842867 = product of:
  0.0357643 = sum of:
    0.01886051 = weight(_text_:on in 1605) [ClassicSimilarity], result of:
      0.01886051 = score(doc=1605,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 1605, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.03380758 = score(doc=1605,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 1605, 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=1605)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

0.020160122 = product of:
  0.030240182 = sum of:
    0.013336393 = weight(_text_:on in 109) [ClassicSimilarity], result of:
      0.013336393 = score(doc=109,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 109, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=109)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 109) [ClassicSimilarity], result of:
          0.03380758 = score(doc=109,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 109, 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=109)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This chapter shows that the wider use of Web search engines, reconsidering the theoretical and methodological frameworks to grasp new information practices. Beginning with an overview of the recent challenges implied by the dynamic nature of the Web, this chapter then traces the information behavior related concepts in order to present the different approaches from the user perspective. The authors pay special attention to the concept of "information practice" and other related concepts such as "use", "activity", and "behavior" largely used in the literature but not always strictly defined. The authors provide an overview of user-oriented studies that are meaningful to understand the different contexts of use of electronic information access systems, focusing on five approaches: the system-oriented approaches, the theories of information seeking, the cognitive and psychological approaches, the management science approaches, and the marketing approaches. Future directions of work are then shaped, including social searching and the ethical, cultural, and political dimensions of Web search engines. The authors conclude considering the importance of Critical theory to better understand the role of Web Search engines in our modern society.

Date

20. 4.2012 13:22:37

0.020160122 = product of:
  0.030240182 = sum of:
    0.013336393 = weight(_text_:on in 1177) [ClassicSimilarity], result of:
      0.013336393 = score(doc=1177,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 1177, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1177)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 1177) [ClassicSimilarity], result of:
          0.03380758 = score(doc=1177,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.19345059 = fieldWeight in 1177, 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=1177)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Search engine users typically engage in multiquery sessions in their quest to fulfill their information needs. Despite a plethora of research findings suggesting that a significant group of users look for information within a specific geographical scope, existing reformulation studies lack a focused analysis of how users reformulate geographic queries. This study comprehensively investigates the ways in which users reformulate such needs in an attempt to fill this gap in the literature. Reformulated sessions were sampled from a query log of a major search engine to extract 2,400 entries that were manually inspected to filter geo sessions. This filter identified 471 search sessions that included geographical intent, and these sessions were analyzed quantitatively and qualitatively. The results revealed that one in five of the users who reformulated their queries were looking for geographically related information. They reformulated their queries by changing the content of the query rather than the structure. Users were not following a unified sequence of modifications and instead performed a single reformulation action. However, in some cases it was possible to anticipate their next move. A number of tasks in geo modifications were identified, including standard, multi-needs, multi-places, and hybrid approaches. The research concludes that it is important to specialize query reformulation studies to focus on particular query types rather than generically analyzing them, as it is apparent that geographic queries have their special reformulation characteristics.

Date

26. 1.2014 18:48:22

0.016690006 = product of:
  0.025035009 = sum of:
    0.013202356 = weight(_text_:on in 2752) [ClassicSimilarity], result of:
      0.013202356 = score(doc=2752,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.120280504 = fieldWeight in 2752, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.02734375 = fieldNorm(doc=2752)
    0.011832653 = product of:
      0.023665305 = sum of:
        0.023665305 = weight(_text_:22 in 2752) [ClassicSimilarity], result of:
          0.023665305 = score(doc=2752,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = queryNorm
            0.1354154 = fieldWeight in 2752, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=2752)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The doctoral thesis of Tatiana Gossen formulates criteria and guidelines on how to design the user interfaces of search engines for children. In her work, the author identifies the conceptual challenges based on own and previous user studies and addresses the changing characteristics of the users by providing a means of adaptation. Additionally, a novel type of search result visualisation for children with cartoon style characters is developed taking children's preference for visual information into account.

Date

1. 2.2016 18:25:22

0.013336394 = product of:
  0.040009182 = sum of:
    0.040009182 = weight(_text_:on in 4537) [ClassicSimilarity], result of:
      0.040009182 = score(doc=4537,freq=18.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.364505 = fieldWeight in 4537, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4537)
  0.33333334 = coord(1/3)

Abstract

Purpose - The purpose of this paper is to test major web search engines on their performance on navigational queries, i.e. searches for homepages. Design/methodology/approach - In total, 100 user queries are posed to six search engines (Google, Yahoo!, MSN, Ask, Seekport, and Exalead). Users described the desired pages, and the results position of these was recorded. Measured success and mean reciprocal rank are calculated. Findings - The performance of the major search engines Google, Yahoo!, and MSN was found to be the best, with around 90 per cent of queries answered correctly. Ask and Exalead performed worse but received good scores as well. Research limitations/implications - All queries were in German, and the German-language interfaces of the search engines were used. Therefore, the results are only valid for German queries. Practical implications - When designing a search engine to compete with the major search engines, care should be taken on the performance on navigational queries. Users can be influenced easily in their quality ratings of search engines based on this performance. Originality/value - This study systematically compares the major search engines on navigational queries and compares the findings with studies on the retrieval effectiveness of the engines on informational queries.

0.013066944 = product of:
  0.03920083 = sum of:
    0.03920083 = weight(_text_:on in 2799) [ClassicSimilarity], result of:
      0.03920083 = score(doc=2799,freq=12.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.35714048 = fieldWeight in 2799, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2799)
  0.33333334 = coord(1/3)

Abstract

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.

0.012573673 = product of:
  0.03772102 = sum of:
    0.03772102 = weight(_text_:on in 3567) [ClassicSimilarity], result of:
      0.03772102 = score(doc=3567,freq=16.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.3436586 = fieldWeight in 3567, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3567)
  0.33333334 = coord(1/3)

Abstract

In an online experiment using a representative sample of the German online population (n = 1.000), we compare users' selection behaviour on two versions of the same Google search engine results page (SERP), one showing advertisements and organic results, the other showing organic results only. Selection behaviour is analyzed in relation to users' knowledge on Google's business model, on SERP design, and on these users' actual performance in marking advertisements on SERPs correctly. We find that users who were not able to mark ads correctly selected ads significantly more often. This leads to the conclusion that ads need to be labeled more clearly, and that there is a need for more information literacy in search engine users.

0.0124473 = product of:
  0.0373419 = sum of:
    0.0373419 = weight(_text_:on in 4115) [ClassicSimilarity], result of:
      0.0373419 = score(doc=4115,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.34020463 = fieldWeight in 4115, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4115)
  0.33333334 = coord(1/3)

Abstract

This representative study of German search engine users (N?=?1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task-based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users can reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results.

0.011928434 = product of:
  0.0357853 = sum of:
    0.0357853 = weight(_text_:on in 4437) [ClassicSimilarity], result of:
      0.0357853 = score(doc=4437,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.32602316 = fieldWeight in 4437, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=4437)
  0.33333334 = coord(1/3)

Abstract

This site provides a collection of schemas, i.e., html tags, that webmasters can use to markup their pages in ways recognized by major search providers. Search engines including Bing, Google and Yahoo! rely on this markup to improve the display of search results, making it easier for people to find the right web pages. Many sites are generated from structured data, which is often stored in databases. When this data is formatted into HTML, it becomes very difficult to recover the original structured data. Many applications, especially search engines, can benefit greatly from direct access to this structured data. On-page markup enables search engines to understand the information on web pages and provide richer search results in order to make it easier for users to find relevant information on the web. Markup can also enable new tools and applications that make use of the structure. A shared markup vocabulary makes easier for webmasters to decide on a markup schema and get the maximum benefit for their efforts. So, in the spirit of sitemaps.org, Bing, Google and Yahoo! have come together to provide a shared collection of schemas that webmasters can use.

0.011928434 = product of:
  0.0357853 = sum of:
    0.0357853 = weight(_text_:on in 478) [ClassicSimilarity], result of:
      0.0357853 = score(doc=478,freq=10.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.32602316 = fieldWeight in 478, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=478)
  0.33333334 = coord(1/3)

Abstract

"Web Search Engine Research", edited by Dirk Lewandowski, provides an understanding of Web search engines from the unique perspective of Library and Information Science. The book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, reliability of search results, query log analysis, user guidance in the search process, and the influence of search engine optimization (SEO) on results quality. While research in computer science has mainly focused on technical aspects of search engines, LIS research is centred on users' behaviour when using search engines and how this interaction can be evaluated. LIS research provides a unique perspective in intermediating between the technical aspects, user aspects and their impact on their role in knowledge acquisition. This book is directly relevant to researchers and practitioners in library and information science, computer science, including Web researchers.

0.010669115 = product of:
  0.032007344 = sum of:
    0.032007344 = weight(_text_:on in 2885) [ClassicSimilarity], result of:
      0.032007344 = score(doc=2885,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.29160398 = fieldWeight in 2885, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2885)
  0.33333334 = coord(1/3)

Abstract

The study sets out to explore the factors that influence the evaluation of information and the judgments made in the process of finding useful information in web search contexts. Based on a diary study of 2 assigned tasks to search on Google and Google Scholar, factor analysis identified the core constructs of content, relevance, scope, and style, as well as informational and system "ease of use" as influencing the judgment that useful information had been found. Differences were found in the participants' evaluation of information across the search tasks on Google and on Google Scholar when identified by the factors related to both content and ease of use. The findings from this study suggest how searchers might critically evaluate information, and the study identifies a relation between the user's involvement in the information interaction and the influences of the perceived system ease of use and information design.

0.010058938 = product of:
  0.030176813 = sum of:
    0.030176813 = weight(_text_:on in 393) [ClassicSimilarity], result of:
      0.030176813 = score(doc=393,freq=16.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.27492687 = fieldWeight in 393, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=393)
  0.33333334 = coord(1/3)

Abstract

This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.