Search (37 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Suchmaschinen"
  • × year_i:[2010 TO 2020}
  1. Lewandowski, D.; Sünkler, S.: What does Google recommend when you want to compare insurance offerings? (2019) 0.04 
    0.036035076 = product of:
  0.08408184 = sum of:
    0.025943318 = weight(_text_:management in 5288) [ClassicSimilarity], result of:
      0.025943318 = score(doc=5288,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.18620178 = fieldWeight in 5288, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5288)
    0.04413724 = weight(_text_:case in 5288) [ClassicSimilarity], result of:
      0.04413724 = score(doc=5288,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.24286987 = fieldWeight in 5288, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5288)
    0.0140012875 = product of:
      0.028002575 = sum of:
        0.028002575 = weight(_text_:22 in 5288) [ClassicSimilarity], result of:
          0.028002575 = score(doc=5288,freq=2.0), product of:
            0.14475311 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041336425 = 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)
  0.42857143 = coord(3/7)
    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
    Source
    Aslib journal of information management. 71(2019) no.3, S.310-324
  2. Vidinli, I.B.; Ozcan, R.: New query suggestion framework and algorithms : a case study for an educational search engine (2016) 0.03 
    0.030295819 = product of:
  0.10603536 = sum of:
    0.031131983 = weight(_text_:management in 3185) [ClassicSimilarity], result of:
      0.031131983 = score(doc=3185,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.22344214 = fieldWeight in 3185, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.046875 = fieldNorm(doc=3185)
    0.07490338 = weight(_text_:case in 3185) [ClassicSimilarity], result of:
      0.07490338 = score(doc=3185,freq=4.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.41216385 = fieldWeight in 3185, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.046875 = fieldNorm(doc=3185)
  0.2857143 = coord(2/7)
    Abstract
    Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as "comparison of queries". We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method.
    Source
    Information processing and management. 52(2016) no.5, S.733-752
  3. Chaudiron, S.; Ihadjadene, M.: Studying Web search engines from a user perspective : key concepts and main approaches (2012) 0.03 
    0.025801437 = product of:
  0.09030502 = sum of:
    0.025943318 = weight(_text_:management in 109) [ClassicSimilarity], result of:
      0.025943318 = score(doc=109,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.18620178 = fieldWeight in 109, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=109)
    0.06436171 = sum of:
      0.03635913 = weight(_text_:studies in 109) [ClassicSimilarity], result of:
        0.03635913 = score(doc=109,freq=2.0), product of:
          0.16494368 = queryWeight, product of:
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.041336425 = queryNorm
          0.22043361 = fieldWeight in 109, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.0390625 = fieldNorm(doc=109)
      0.028002575 = weight(_text_:22 in 109) [ClassicSimilarity], result of:
        0.028002575 = score(doc=109,freq=2.0), product of:
          0.14475311 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.041336425 = 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)
  0.2857143 = coord(2/7)
    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
  4. Berri, J.; Benlamri, R.: Context-aware mobile search engine (2012) 0.02 
    0.024027621 = product of:
  0.08409667 = sum of:
    0.031131983 = weight(_text_:management in 104) [ClassicSimilarity], result of:
      0.031131983 = score(doc=104,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.22344214 = fieldWeight in 104, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
    0.052964687 = weight(_text_:case in 104) [ClassicSimilarity], result of:
      0.052964687 = score(doc=104,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.29144385 = fieldWeight in 104, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.046875 = fieldNorm(doc=104)
  0.2857143 = coord(2/7)
    Abstract
    Exploiting context information in a web search engine helps fine-tuning web services and applications to deliver custom-made information to end users. While context, including user and environment information, cannot be exploited efficiently in the wired Internet interaction type, it is becoming accessible with the mobile web where users have an intimate relationship with their handsets. In this type of interaction, context plays a significant role enhancing information search and therefore, allowing a search engine to detect relevant content in all digital forms and formats. This chapter proposes a context model and an architecture that promote integration of context information for individuals and social communities to add value to their interaction with the mobile web. The architecture relies on efficient knowledge management of multimedia resources for a wide range of applications and web services. The research is illustrated with a corporate case study showing how efficient context integration improves usability of a mobile search engine.
  5. Thelwall, M.: Assessing web search engines : a webometric approach (2011) 0.02 
    0.021365764 = product of:
  0.074780166 = sum of:
    0.052964687 = weight(_text_:case in 10) [ClassicSimilarity], result of:
      0.052964687 = score(doc=10,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.29144385 = fieldWeight in 10, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.046875 = fieldNorm(doc=10)
    0.021815477 = product of:
      0.043630954 = sum of:
        0.043630954 = weight(_text_:studies in 10) [ClassicSimilarity], result of:
          0.043630954 = score(doc=10,freq=2.0), product of:
            0.16494368 = queryWeight, product of:
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.041336425 = queryNorm
            0.26452032 = fieldWeight in 10, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.046875 = fieldNorm(doc=10)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)
    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.
  6. Lewandowski, D.: Evaluating the retrieval effectiveness of web search engines using a representative query sample (2015) 0.02 
    0.021365764 = product of:
  0.074780166 = sum of:
    0.052964687 = weight(_text_:case in 2157) [ClassicSimilarity], result of:
      0.052964687 = score(doc=2157,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.29144385 = fieldWeight in 2157, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.046875 = fieldNorm(doc=2157)
    0.021815477 = product of:
      0.043630954 = sum of:
        0.043630954 = weight(_text_:studies in 2157) [ClassicSimilarity], result of:
          0.043630954 = score(doc=2157,freq=2.0), product of:
            0.16494368 = queryWeight, product of:
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.041336425 = queryNorm
            0.26452032 = fieldWeight in 2157, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.046875 = fieldNorm(doc=2157)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)
    Abstract
    Search engine retrieval effectiveness studies are usually small scale, using only limited query samples. Furthermore, queries are selected by the researchers. We address these issues by taking a random representative sample of 1,000 informational and 1,000 navigational queries from a major German search engine and comparing Google's and Bing's results based on this sample. Jurors were found through crowdsourcing, and data were collected using specialized software, the Relevance Assessment Tool (RAT). We found that although Google outperforms Bing in both query types, the difference in the performance for informational queries was rather low. However, for navigational queries, Google found the correct answer in 95.3% of cases, whereas Bing only found the correct answer 76.6% of the time. We conclude that search engine performance on navigational queries is of great importance, because users in this case can clearly identify queries that have returned correct results. So, performance on this query type may contribute to explaining user satisfaction with search engines.
  7. Gencosman, B.C.; Ozmutlu, H.C.; Ozmutlu, S.: Character n-gram application for automatic new topic identification (2014) 0.01 
    0.014758031 = product of:
  0.051653106 = sum of:
    0.025943318 = weight(_text_:management in 2688) [ClassicSimilarity], result of:
      0.025943318 = score(doc=2688,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.18620178 = fieldWeight in 2688, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2688)
    0.025709787 = product of:
      0.051419575 = sum of:
        0.051419575 = weight(_text_:studies in 2688) [ClassicSimilarity], result of:
          0.051419575 = score(doc=2688,freq=4.0), product of:
            0.16494368 = queryWeight, product of:
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.041336425 = queryNorm
            0.3117402 = fieldWeight in 2688, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2688)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)
    Abstract
    The widespread availability of the Internet and the variety of Internet-based applications have resulted in a significant increase in the amount of web pages. Determining the behaviors of search engine users has become a critical step in enhancing search engine performance. Search engine user behaviors can be determined by content-based or content-ignorant algorithms. Although many content-ignorant studies have been performed to automatically identify new topics, previous results have demonstrated that spelling errors can cause significant errors in topic shift estimates. In this study, we focused on minimizing the number of wrong estimates that were based on spelling errors. We developed a new hybrid algorithm combining character n-gram and neural network methodologies, and compared the experimental results with results from previous studies. For the FAST and Excite datasets, the proposed algorithm improved topic shift estimates by 6.987% and 2.639%, respectively. Moreover, we analyzed the performance of the character n-gram method in different aspects including the comparison with Levenshtein edit-distance method. The experimental results demonstrated that the character n-gram method outperformed to the Levensthein edit distance method in terms of topic identification.
    Source
    Information processing and management. 50(2014) no.6, S.821-856
  8. Huvila, I.: Affective capitalism of knowing and the society of search engine (2016) 0.01 
    0.013695294 = product of:
  0.047933526 = sum of:
    0.031131983 = weight(_text_:management in 3246) [ClassicSimilarity], result of:
      0.031131983 = score(doc=3246,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.22344214 = fieldWeight in 3246, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.046875 = fieldNorm(doc=3246)
    0.016801544 = product of:
      0.033603087 = sum of:
        0.033603087 = weight(_text_:22 in 3246) [ClassicSimilarity], result of:
          0.033603087 = score(doc=3246,freq=2.0), product of:
            0.14475311 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041336425 = 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)
  0.2857143 = coord(2/7)
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 68(2016) no.5, S.566-588
  9. Roy, R.S.; Agarwal, S.; Ganguly, N.; Choudhury, M.: Syntactic complexity of Web search queries through the lenses of language models, networks and users (2016) 0.01 
    0.012606538 = product of:
  0.044122882 = sum of:
    0.025943318 = weight(_text_:management in 3188) [ClassicSimilarity], result of:
      0.025943318 = score(doc=3188,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.18620178 = fieldWeight in 3188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
    0.018179566 = product of:
      0.03635913 = sum of:
        0.03635913 = weight(_text_:studies in 3188) [ClassicSimilarity], result of:
          0.03635913 = score(doc=3188,freq=2.0), product of:
            0.16494368 = queryWeight, product of:
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.041336425 = queryNorm
            0.22043361 = fieldWeight in 3188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.9902744 = idf(docFreq=2222, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3188)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)
    Abstract
    Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.
    Source
    Information processing and management. 52(2016) no.5, S.923-948
  10. Alqaraleh, S.; Ramadan, O.; Salamah, M.: Efficient watcher based web crawler design (2015) 0.01 
    0.011412744 = product of:
  0.039944604 = sum of:
    0.025943318 = weight(_text_:management in 1627) [ClassicSimilarity], result of:
      0.025943318 = score(doc=1627,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.18620178 = fieldWeight in 1627, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1627)
    0.0140012875 = product of:
      0.028002575 = sum of:
        0.028002575 = weight(_text_:22 in 1627) [ClassicSimilarity], result of:
          0.028002575 = score(doc=1627,freq=2.0), product of:
            0.14475311 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041336425 = queryNorm
            0.19345059 = fieldWeight in 1627, 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=1627)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 67(2015) no.6, S.663-686
  11. Sachse, J.: ¬The influence of snippet length on user behavior in mobile web search (2019) 0.01 
    0.011412744 = product of:
  0.039944604 = sum of:
    0.025943318 = weight(_text_:management in 5493) [ClassicSimilarity], result of:
      0.025943318 = score(doc=5493,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.18620178 = fieldWeight in 5493, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5493)
    0.0140012875 = product of:
      0.028002575 = sum of:
        0.028002575 = weight(_text_:22 in 5493) [ClassicSimilarity], result of:
          0.028002575 = score(doc=5493,freq=2.0), product of:
            0.14475311 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041336425 = 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)
  0.2857143 = coord(2/7)
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 71(2019) no.3, S.325-343
  12. Aloteibi, S.; Sanderson, M.: Analyzing geographic query reformulation : an exploratory study (2014) 0.01 
    0.011346022 = product of:
  0.079422146 = sum of:
    0.079422146 = sum of:
      0.051419575 = weight(_text_:studies in 1177) [ClassicSimilarity], result of:
        0.051419575 = score(doc=1177,freq=4.0), product of:
          0.16494368 = queryWeight, product of:
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.041336425 = queryNorm
          0.3117402 = fieldWeight in 1177, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1177)
      0.028002575 = weight(_text_:22 in 1177) [ClassicSimilarity], result of:
        0.028002575 = score(doc=1177,freq=2.0), product of:
          0.14475311 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.041336425 = 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)
  0.14285715 = coord(1/7)
    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
  13. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01 
    0.00919453 = product of:
  0.06436171 = sum of:
    0.06436171 = sum of:
      0.03635913 = weight(_text_:studies in 1605) [ClassicSimilarity], result of:
        0.03635913 = score(doc=1605,freq=2.0), product of:
          0.16494368 = queryWeight, product of:
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.041336425 = queryNorm
          0.22043361 = fieldWeight in 1605, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1605)
      0.028002575 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
        0.028002575 = score(doc=1605,freq=2.0), product of:
          0.14475311 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.041336425 = 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)
  0.14285715 = coord(1/7)
    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
  14. Gossen, T.: Search engines for children : search user interfaces and information-seeking behaviour (2016) 0.01 
    0.009097843 = product of:
  0.0636849 = sum of:
    0.0636849 = sum of:
      0.0440831 = weight(_text_:studies in 2752) [ClassicSimilarity], result of:
        0.0440831 = score(doc=2752,freq=6.0), product of:
          0.16494368 = queryWeight, product of:
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.041336425 = queryNorm
          0.26726153 = fieldWeight in 2752, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.9902744 = idf(docFreq=2222, maxDocs=44218)
            0.02734375 = fieldNorm(doc=2752)
      0.019601801 = weight(_text_:22 in 2752) [ClassicSimilarity], result of:
        0.019601801 = score(doc=2752,freq=2.0), product of:
          0.14475311 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.041336425 = 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)
  0.14285715 = coord(1/7)
    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.
    Content
    Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work
    Date
    1. 2.2016 18:25:22
  15. Berget, G.; Sandnes, F.E.: Do autocomplete functions reduce the impact of dyslexia on information-searching behavior? : the case of Google (2016) 0.01 
    0.007566384 = product of:
  0.052964687 = sum of:
    0.052964687 = weight(_text_:case in 3112) [ClassicSimilarity], result of:
      0.052964687 = score(doc=3112,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.29144385 = fieldWeight in 3112, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.046875 = fieldNorm(doc=3112)
  0.14285715 = coord(1/7)
  16. Fu, T.; Abbasi, A.; Chen, H.: ¬A focused crawler for Dark Web forums (2010) 0.01 
    0.00630532 = product of:
  0.04413724 = sum of:
    0.04413724 = weight(_text_:case in 3471) [ClassicSimilarity], result of:
      0.04413724 = score(doc=3471,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.24286987 = fieldWeight in 3471, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3471)
  0.14285715 = coord(1/7)
    Abstract
    The unprecedented growth of the Internet has given rise to the Dark Web, the problematic facet of the Web associated with cybercrime, hate, and extremism. Despite the need for tools to collect and analyze Dark Web forums, the covert nature of this part of the Internet makes traditional Web crawling techniques insufficient for capturing such content. In this study, we propose a novel crawling system designed to collect Dark Web forum content. The system uses a human-assisted accessibility approach to gain access to Dark Web forums. Several URL ordering features and techniques enable efficient extraction of forum postings. The system also includes an incremental crawler coupled with a recall-improvement mechanism intended to facilitate enhanced retrieval and updating of collected content. Experiments conducted to evaluate the effectiveness of the human-assisted accessibility approach and the recall-improvement-based, incremental-update procedure yielded favorable results. The human-assisted approach significantly improved access to Dark Web forums while the incremental crawler with recall improvement also outperformed standard periodic- and incremental-update approaches. Using the system, we were able to collect over 100 Dark Web forums from three regions. A case study encompassing link and content analysis of collected forums was used to illustrate the value and importance of gathering and analyzing content from such online communities.
  17. Akhigbe, B.I.; Afolabi, B.S.; Adagunodo, E.R.: Modelling user-centered attributes : the Web search engine as a case (2015) 0.01 
    0.00630532 = product of:
  0.04413724 = sum of:
    0.04413724 = weight(_text_:case in 2100) [ClassicSimilarity], result of:
      0.04413724 = score(doc=2100,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.24286987 = fieldWeight in 2100, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2100)
  0.14285715 = coord(1/7)
  18. Zhitomirsky-Geffet, M.; Bar-Ilan, J.; Levene, M.: Analysis of change in users' assessment of search results over time (2017) 0.01 
    0.00630532 = product of:
  0.04413724 = sum of:
    0.04413724 = weight(_text_:case in 3593) [ClassicSimilarity], result of:
      0.04413724 = score(doc=3593,freq=2.0), product of:
        0.18173204 = queryWeight, product of:
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.041336425 = queryNorm
        0.24286987 = fieldWeight in 3593, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.3964143 = idf(docFreq=1480, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3593)
  0.14285715 = coord(1/7)
    Abstract
    We present the first systematic study of the influence of time on user judgements for rankings and relevance grades of web search engine results. The goal of this study is to evaluate the change in user assessment of search results and explore how users' judgements change. To this end, we conducted a large-scale user study with 86 participants who evaluated 2 different queries and 4 diverse result sets twice with an interval of 2 months. To analyze the results we investigate whether 2 types of patterns of user behavior from the theory of categorical thinking hold for the case of evaluation of search results: (a) coarseness and (b) locality. To quantify these patterns we devised 2 new measures of change in user judgements and distinguish between local (when users swap between close ranks and relevance values) and nonlocal changes. Two types of judgements were considered in this study: (a) relevance on a 4-point scale, and (b) ranking on a 10-point scale without ties. We found that users tend to change their judgements of the results over time in about 50% of cases for relevance and in 85% of cases for ranking. However, the majority of these changes were local.
  19. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.00 
    0.004447426 = product of:
  0.031131983 = sum of:
    0.031131983 = weight(_text_:management in 2799) [ClassicSimilarity], result of:
      0.031131983 = score(doc=2799,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.22344214 = fieldWeight in 2799, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.046875 = fieldNorm(doc=2799)
  0.14285715 = coord(1/7)
    Source
    Information processing and management. 50(2014) no.2, S.416-425
  20. Kucukyilmaz, T.; Cambazoglu, B.B.; Aykanat, C.; Baeza-Yates, R.: ¬A machine learning approach for result caching in web search engines (2017) 0.00 
    0.004447426 = product of:
  0.031131983 = sum of:
    0.031131983 = weight(_text_:management in 5100) [ClassicSimilarity], result of:
      0.031131983 = score(doc=5100,freq=2.0), product of:
        0.13932906 = queryWeight, product of:
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.041336425 = queryNorm
        0.22344214 = fieldWeight in 5100, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.3706124 = idf(docFreq=4130, maxDocs=44218)
          0.046875 = fieldNorm(doc=5100)
  0.14285715 = coord(1/7)
    Source
    Information processing and management. 53(2017) no.4, S.834-850

Authors

Types

  • a 35
  • el 3
  • m 2
  • More… Less…

Themes

Subjects

Classifications