Search (54 results, page 1 of 3)

0.085817665 = product of:
  0.17163533 = sum of:
    0.15646377 = weight(_text_:engines in 109) [ClassicSimilarity], result of:
      0.15646377 = score(doc=109,freq=12.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.68751884 = fieldWeight in 109, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=109)
    0.015171562 = product of:
      0.030343125 = sum of:
        0.030343125 = weight(_text_:22 in 109) [ClassicSimilarity], result of:
          0.030343125 = score(doc=109,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

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

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64435.

Source

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

0.0754849 = product of:
  0.1509698 = sum of:
    0.13276392 = weight(_text_:engines in 105) [ClassicSimilarity], result of:
      0.13276392 = score(doc=105,freq=6.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.58337915 = fieldWeight in 105, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=105)
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 105) [ClassicSimilarity], result of:
          0.036411747 = score(doc=105,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

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

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64429.

Source

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

0.06330358 = product of:
  0.12660716 = sum of:
    0.10840129 = weight(_text_:engines in 93) [ClassicSimilarity], result of:
      0.10840129 = score(doc=93,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 93, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=93)
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 93) [ClassicSimilarity], result of:
          0.036411747 = score(doc=93,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

Date

17. 4.2012 14:25:22

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64421.

Source

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

0.06330358 = product of:
  0.12660716 = sum of:
    0.10840129 = weight(_text_:engines in 108) [ClassicSimilarity], result of:
      0.10840129 = score(doc=108,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 108, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=108)
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 108) [ClassicSimilarity], result of:
          0.036411747 = score(doc=108,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)

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

0.06290409 = product of:
  0.12580818 = sum of:
    0.110636614 = weight(_text_:engines in 444) [ClassicSimilarity], result of:
      0.110636614 = score(doc=444,freq=6.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.4861493 = fieldWeight in 444, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=444)
    0.015171562 = product of:
      0.030343125 = sum of:
        0.030343125 = weight(_text_:22 in 444) [ClassicSimilarity], result of:
          0.030343125 = score(doc=444,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

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

0.054200646 = product of:
  0.21680258 = sum of:
    0.21680258 = weight(_text_:engines in 110) [ClassicSimilarity], result of:
      0.21680258 = score(doc=110,freq=16.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.9526541 = fieldWeight in 110, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=110)
  0.25 = coord(1/4)

Abstract

Search engines are the major means of information retrieval over the Internet. People's dependence on them increases over time as SEs introduce new and sophisticated technologies. The developments in the Artificial Intelligence (AI) will transform the current search engines Artificial Intelligence Enabled Search Engines (AIESE). Search engines already play a critical role in classifying, sorting and delivering the information over the Internet. However, as Internet's mainstream role becomes more apparent and AI technology increases the sophistication of the tools of the SEs, their roles will become much more critical. Since, the future of search engines are examined, the technological singularity concept is analyzed in detail. Second and third order indirect side effects are analyzed. A four-stage evolution-model is suggested.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64436.

Source

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

0.052752987 = product of:
  0.10550597 = sum of:
    0.09033441 = weight(_text_:engines in 5288) [ClassicSimilarity], result of:
      0.09033441 = score(doc=5288,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39693922 = fieldWeight in 5288, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5288)
    0.015171562 = product of:
      0.030343125 = sum of:
        0.030343125 = weight(_text_:22 in 5288) [ClassicSimilarity], result of:
          0.030343125 = score(doc=5288,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

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

0.04790705 = product of:
  0.1916282 = sum of:
    0.1916282 = weight(_text_:engines in 4537) [ClassicSimilarity], result of:
      0.1916282 = score(doc=4537,freq=18.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.8420352 = fieldWeight in 4537, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4537)
  0.25 = coord(1/4)

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.

0.04742858 = product of:
  0.09485716 = sum of:
    0.07665128 = weight(_text_:engines in 3246) [ClassicSimilarity], result of:
      0.07665128 = score(doc=3246,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.33681408 = fieldWeight in 3246, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=3246)
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 3246) [ClassicSimilarity], result of:
          0.036411747 = score(doc=3246,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

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

0.046939135 = product of:
  0.18775654 = sum of:
    0.18775654 = weight(_text_:engines in 106) [ClassicSimilarity], result of:
      0.18775654 = score(doc=106,freq=12.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.82502264 = fieldWeight in 106, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=106)
  0.25 = coord(1/4)

Abstract

This chapter presents a theoretical framework for evaluating next generation search engines. The author focuses on search engines whose results presentation is enriched with additional information and does not merely present the usual list of "10 blue links," that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64437.

Source

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

0.039523818 = product of:
  0.079047635 = sum of:
    0.06387607 = weight(_text_:engines in 1605) [ClassicSimilarity], result of:
      0.06387607 = score(doc=1605,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 1605, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.015171562 = product of:
      0.030343125 = sum of:
        0.030343125 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.030343125 = score(doc=1605,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = 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)
  0.5 = coord(2/4)

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

0.033800043 = product of:
  0.13520017 = sum of:
    0.13520017 = weight(_text_:engines in 393) [ClassicSimilarity], result of:
      0.13520017 = score(doc=393,freq=14.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.59408426 = fieldWeight in 393, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.03125 = fieldNorm(doc=393)
  0.25 = coord(1/4)

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.

0.03319098 = product of:
  0.13276392 = sum of:
    0.13276392 = weight(_text_:engines in 95) [ClassicSimilarity], result of:
      0.13276392 = score(doc=95,freq=6.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.58337915 = fieldWeight in 95, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=95)
  0.25 = coord(1/4)

Abstract

In this chapter, the authors describe the key indexing components of today's web search engines. As the World Wide Web has grown, the systems and methods for indexing have changed significantly. The authors present the data structures used, the features extracted, the infrastructure needed, and the options available for designing a brand new search engine. Techniques are highlighted that improve relevance of results, discuss trade-offs to best utilize machine resources, and cover distributed processing concepts in this context. In particular, the authors delve into the topics of indexing phrases instead of terms, storage in memory vs. on disk, and data partitioning. Some thoughts on information organization for the newly emerging data-forms conclude the chapter.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64418.

Source

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

0.03319098 = product of:
  0.13276392 = sum of:
    0.13276392 = weight(_text_:engines in 96) [ClassicSimilarity], result of:
      0.13276392 = score(doc=96,freq=6.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.58337915 = fieldWeight in 96, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=96)
  0.25 = coord(1/4)

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64420.

Source

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

0.031938035 = product of:
  0.12775214 = sum of:
    0.12775214 = weight(_text_:engines in 4140) [ClassicSimilarity], result of:
      0.12775214 = score(doc=4140,freq=8.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.5613568 = fieldWeight in 4140, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4140)
  0.25 = coord(1/4)

Abstract

Despite improvements in their capabilities, search engines still fail to provide users with only relevant results. One reason is that most search engines implement a "one size fits all" approach that ignores personal preferences when retrieving the results of a user's query. Recent studies (Smyth, 2010) have elaborated the importance of personalizing search results and have proposed integrating recommender system methods for enhancing results using contextual and extrinsic information that might indicate the user's actual needs. In this article, we review recommender system methods used for personalizing and improving search results and examine the effect of two such methods that are merged for this purpose. One method is based on collaborative users' knowledge; the second integrates information from the user's social network. We propose new methods for collaborative-and social-based search and demonstrate that each of these methods, when separately applied, produce more accurate search results than does a purely keyword-based search engine (referred to as "standard search engine"), where the social search engine is more accurate than is the collaborative one. However, separately applied, these methods do not produce a sufficient number of results (low coverage). Nevertheless, merging these methods with those implemented by standard search engines overcomes the low-coverage problem and produces personalized results for users that display significantly more accurate results while also providing sufficient coverage than do standard search engines. The improvement, however, is significant only for topics for which the diversity of terms used for queries among users is low.

0.031938035 = product of:
  0.12775214 = sum of:
    0.12775214 = weight(_text_:engines in 100) [ClassicSimilarity], result of:
      0.12775214 = score(doc=100,freq=8.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.5613568 = fieldWeight in 100, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=100)
  0.25 = coord(1/4)

Abstract

Over the last years, research and industry players have become increasingly interested in analyzing opinions and sentiments expressed on the social media web for product marketing and business intelligence. In order to adapt to this need search engines not only have to be able to retrieve lists of documents but to directly access, analyze, and interpret topics and opinions. This article covers an intermediate phase of the ongoing industrial research project 'DoXa' aiming at developing a semantic opinion and sentiment mining search engine for the French language. The DoXa search engine enables topic related opinion and sentiment extraction beyond positive and negative polarity using rich linguistic resources. Centering the work on two distinct business use cases, the authors analyze both unstructured Web 2.0 contents (e.g., blogs and forums) and structured questionnaire data sets. The focus is on discovering hidden patterns in the data. To this end, the authors present work in progress on opinion topic relation extraction and visual analytics, linguistic resource construction as well as the combination of OLAP technology with semantic search.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64426.

Source

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

0.027100323 = product of:
  0.10840129 = sum of:
    0.10840129 = weight(_text_:engines in 10) [ClassicSimilarity], result of:
      0.10840129 = score(doc=10,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 10, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=10)
  0.25 = coord(1/4)

Abstract

Information Retrieval (IR) research typically evaluates search systems in terms of the standard precision, recall and F-measures to weight the relative importance of precision and recall (e.g. van Rijsbergen, 1979). All of these assess the extent to which the system returns good matches for a query. In contrast, webometric measures are designed specifically for web search engines and are designed to monitor changes in results over time and various aspects of the internal logic of the way in which search engine select the results to be returned. This chapter introduces a range of webometric measurements and illustrates them with case studies of Google, Bing and Yahoo! This is a very fertile area for simple and complex new investigations into search engine results.

0.027100323 = product of:
  0.10840129 = sum of:
    0.10840129 = weight(_text_:engines in 94) [ClassicSimilarity], result of:
      0.10840129 = score(doc=94,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 94, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=94)
  0.25 = coord(1/4)

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64419.

Source

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

0.027100323 = product of:
  0.10840129 = sum of:
    0.10840129 = weight(_text_:engines in 102) [ClassicSimilarity], result of:
      0.10840129 = score(doc=102,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 102, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=102)
  0.25 = coord(1/4)

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64427.

Source

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

0.027100323 = product of:
  0.10840129 = sum of:
    0.10840129 = weight(_text_:engines in 104) [ClassicSimilarity], result of:
      0.10840129 = score(doc=104,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 104, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
  0.25 = coord(1/4)

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64433.

Source

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