Search (89 results, page 1 of 5)

0.14173794 = product of:
  0.2126069 = sum of:
    0.05889038 = weight(_text_:query in 5493) [ClassicSimilarity], result of:
      0.05889038 = score(doc=5493,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.25674784 = fieldWeight in 5493, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5493)
    0.15371652 = sum of:
      0.120283864 = weight(_text_:page in 5493) [ClassicSimilarity], result of:
        0.120283864 = score(doc=5493,freq=4.0), product of:
          0.27565226 = queryWeight, product of:
            5.5854197 = idf(docFreq=450, maxDocs=44218)
            0.049352113 = queryNorm
          0.4363609 = fieldWeight in 5493, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            5.5854197 = idf(docFreq=450, maxDocs=44218)
            0.0390625 = fieldNorm(doc=5493)
      0.03343265 = weight(_text_:22 in 5493) [ClassicSimilarity], result of:
        0.03343265 = score(doc=5493,freq=2.0), product of:
          0.1728227 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.049352113 = 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)
  0.6666667 = 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

0.12788323 = product of:
  0.19182482 = sum of:
    0.13168289 = weight(_text_:query in 1081) [ClassicSimilarity], result of:
      0.13168289 = score(doc=1081,freq=10.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.5741056 = fieldWeight in 1081, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1081)
    0.060141932 = product of:
      0.120283864 = sum of:
        0.120283864 = weight(_text_:page in 1081) [ClassicSimilarity], result of:
          0.120283864 = score(doc=1081,freq=4.0), product of:
            0.27565226 = queryWeight, product of:
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.049352113 = queryNorm
            0.4363609 = fieldWeight in 1081, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1081)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The aim of the work described in this paper is to evaluate the influencing effects of query-biased summaries in web searching. For this purpose, a summarisation system has been developed, and a summary tailored to the user's query is generated automatically for each document retrieved. The system aims to provide both a better means of assessing document relevance than titles or abstracts typical of many web search result lists. Through visiting each result page at retrieval-time, the system provides the user with an idea of the current page content and thus deals with the dynamic nature of the web. To examine the effectiveness of this approach, a task-oriented, comparative evaluation between four different web retrieval systems was performed; two that use query-biased summarisation, and two that use the standard ranked titles/abstracts approach. The results from the evaluation indicate that query-biased summarisation techniques appear to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach. The same methodology was used to compare the effectiveness of two of the web's major search engines; AltaVista and Google.

0.10210096 = product of:
  0.15315144 = sum of:
    0.09422461 = weight(_text_:query in 1264) [ClassicSimilarity], result of:
      0.09422461 = score(doc=1264,freq=8.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.41079655 = fieldWeight in 1264, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.03125 = fieldNorm(doc=1264)
    0.058926824 = product of:
      0.11785365 = sum of:
        0.11785365 = weight(_text_:page in 1264) [ClassicSimilarity], result of:
          0.11785365 = score(doc=1264,freq=6.0), product of:
            0.27565226 = queryWeight, product of:
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.049352113 = queryNorm
            0.42754465 = fieldWeight in 1264, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.03125 = fieldNorm(doc=1264)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).

0.100648284 = product of:
  0.15097243 = sum of:
    0.09994029 = weight(_text_:query in 3294) [ClassicSimilarity], result of:
      0.09994029 = score(doc=3294,freq=4.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.43571556 = fieldWeight in 3294, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=3294)
    0.051032126 = product of:
      0.10206425 = sum of:
        0.10206425 = weight(_text_:page in 3294) [ClassicSimilarity], result of:
          0.10206425 = score(doc=3294,freq=2.0), product of:
            0.27565226 = queryWeight, product of:
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.049352113 = queryNorm
            0.37026453 = fieldWeight in 3294, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.046875 = fieldNorm(doc=3294)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This study focuses on the impact of age, prior domain knowledge and cognitive abilities on performance, query production and navigation strategies during information searching. Twenty older adults and nineteen young adults had to answer 12 information search problems of varying nature within two domain knowledge: health and manga. In each domain, participants had to perform two simple fact-finding problems (keywords provided and answer directly accessible on the search engine results page), two difficult fact-finding problems (keywords had to be inferred) and two open-ended information search problems (multiple answers possible and navigation necessary). Results showed that prior domain knowledge helped older adults improve navigation (i.e. reduced the number of webpages visited and thus decreased the feeling of disorientation), query production and reformulation (i.e. they formulated semantically more specific queries, and they inferred a greater number of new keywords).

0.09893282 = product of:
  0.14839922 = sum of:
    0.13168289 = weight(_text_:query in 1177) [ClassicSimilarity], result of:
      0.13168289 = score(doc=1177,freq=10.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.5741056 = fieldWeight in 1177, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1177)
    0.016716326 = product of:
      0.03343265 = sum of:
        0.03343265 = weight(_text_:22 in 1177) [ClassicSimilarity], result of:
          0.03343265 = score(doc=1177,freq=2.0), product of:
            0.1728227 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049352113 = 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

0.09460085 = product of:
  0.14190127 = sum of:
    0.047112305 = weight(_text_:query in 1628) [ClassicSimilarity], result of:
      0.047112305 = score(doc=1628,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.20539828 = fieldWeight in 1628, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.03125 = fieldNorm(doc=1628)
    0.09478896 = sum of:
      0.06804284 = weight(_text_:page in 1628) [ClassicSimilarity], result of:
        0.06804284 = score(doc=1628,freq=2.0), product of:
          0.27565226 = queryWeight, product of:
            5.5854197 = idf(docFreq=450, maxDocs=44218)
            0.049352113 = queryNorm
          0.24684301 = fieldWeight in 1628, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.5854197 = idf(docFreq=450, maxDocs=44218)
            0.03125 = fieldNorm(doc=1628)
      0.026746122 = weight(_text_:22 in 1628) [ClassicSimilarity], result of:
        0.026746122 = score(doc=1628,freq=2.0), product of:
          0.1728227 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.049352113 = queryNorm
          0.15476047 = fieldWeight in 1628, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.03125 = fieldNorm(doc=1628)
  0.6666667 = coord(2/3)

Abstract

Purpose - The purpose of this paper is to propose the Collaborative Search System that attempts to achieve collaboration by implicitly identifying and reflecting search behaviour of collaborators in an academic network that is automatically and dynamically formed. By using the constructed Collaborative Hit Matrix (CHM), results are obtained that are based on the search behaviour and earned preferences of specialist communities of researchers, which are relevant to the user's need and reduce the time spent on bad links. Design/methodology/approach - By using the Digital Bibliography Library Project (DBLP), the research communities are formed implicitly and dynamically based on the users' research presence in the search environment and in the publication scenario, which is also used to assign users' roles and establish links between the users. The CHM, to store the hit count and hit list of page results for queries, is also constructed and updated after every search session to enhance the collaborative search among the researchers. Findings - The implicit researchers community formation, the assignment and dynamic updating of roles of the researchers based on research, search presence and search behaviour on the web as well as the usage of these roles during Collaborative Web Search have highly improved the relevancy of results. The CHM that holds the collaborative responses provided by the researchers on the search query results to support searching distinguishes this system from others. Thus the proposed system considerably improves the relevancy and reduces the time spent on bad links, thus improving recall and precision. Originality/value - The research findings illustrate the better performance of the system, by connecting researchers working in the same field and allowing them to help each other in a web search environment.

Date

20. 1.2015 18:30:22

0.08387356 = product of:
  0.12581034 = sum of:
    0.08328357 = weight(_text_:query in 495) [ClassicSimilarity], result of:
      0.08328357 = score(doc=495,freq=4.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.3630963 = fieldWeight in 495, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=495)
    0.04252677 = product of:
      0.08505354 = sum of:
        0.08505354 = weight(_text_:page in 495) [ClassicSimilarity], result of:
          0.08505354 = score(doc=495,freq=2.0), product of:
            0.27565226 = queryWeight, product of:
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.049352113 = queryNorm
            0.30855376 = fieldWeight in 495, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.0390625 = fieldNorm(doc=495)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

In information seeking and retrieval research, researchers often collect data about users' behaviors to predict task characteristics and personalize information for users. The reliability of user behavior may be directly influenced by data collection methods. This article reports on a mixed-methods study examining the impact of study setting (laboratory setting vs. remote setting) and task authenticity (authentic task vs. simulated task) on users' online browsing and searching behaviors. Thirty-six undergraduate participants finished one lab session and one remote session in which they completed one authentic and one simulated task. Using log data collected from 144 task sessions, this study demonstrates that the synthetic lab study setting and simulated tasks had significant influences mostly on behaviors related to content pages (e.g., page dwell time, number of pages visited per task). Meanwhile, first-query behaviors were less affected by study settings or task authenticity than whole-session behaviors, indicating the reliability of using first-query behaviors in task prediction. Qualitative interviews reveal why users were influenced. This study addresses methodological limitations in existing research and provides new insights and implications for researchers who collect online user search behavioral data.

0.07914498 = product of:
  0.11871746 = sum of:
    0.10200114 = weight(_text_:query in 2680) [ClassicSimilarity], result of:
      0.10200114 = score(doc=2680,freq=6.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.44470036 = fieldWeight in 2680, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2680)
    0.016716326 = product of:
      0.03343265 = sum of:
        0.03343265 = weight(_text_:22 in 2680) [ClassicSimilarity], result of:
          0.03343265 = score(doc=2680,freq=2.0), product of:
            0.1728227 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049352113 = queryNorm
            0.19345059 = fieldWeight in 2680, 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=2680)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This study addresses the impact of domain expertise (i.e. of prior knowledge of the domain) on the performance and query strategies used by users while searching for information. Twenty-four experts (psychology students) and 24 non-experts (students from other disciplines) had to search for psychology information from the Universalis website in order to perform six information problems of varying complexity: two simple problems (the keywords required to complete the task were provided in the problem statement), two more difficult problems (the keywords required had to be inferred) and two impossible problems (no answer was provided by the website). The results showed that participants with prior knowledge in the domain (experts in psychology) performed better (i.e. reached more correct answers after shorter search times) than non-experts. This difference was stronger as the complexity of the problems increased. This study also showed that experts and non-experts displayed different query strategies. Experts reformulated the impossible problems more often than non-experts, because they produced new queries with psychology-related keywords. The participants rarely used thematic category tool and when they did so this did not enhance their performance.

Date

25. 1.2016 18:46:22

0.06761144 = product of:
  0.101417154 = sum of:
    0.05889038 = weight(_text_:query in 3588) [ClassicSimilarity], result of:
      0.05889038 = score(doc=3588,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.25674784 = fieldWeight in 3588, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3588)
    0.04252677 = product of:
      0.08505354 = sum of:
        0.08505354 = weight(_text_:page in 3588) [ClassicSimilarity], result of:
          0.08505354 = score(doc=3588,freq=2.0), product of:
            0.27565226 = queryWeight, product of:
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.049352113 = queryNorm
            0.30855376 = fieldWeight in 3588, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5854197 = idf(docFreq=450, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3588)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Evaluation of information during information problem-solving processes already starts when trying to select the appropriate search result on a search engine results page (SERP). Up to now, research has mainly focused on the evaluation of webpages, while the evaluation of SERPs received less attention. Furthermore, task complexity is often not taken into account. A within-subjects design was used to study the influence of task complexity on search query formulation, evaluation of search results, and task performance. Three search tasks were used: a fact-finding, cause-effect, and a controversial topic task. To measure perceptual search processes, we used a combination of log files, eye-tracking data, answer forms, and think-aloud protocols. The results reveal that an increase in task complexity results in more search queries and used keywords, more time to formulate search queries, and more considered search results on the SERPs. Furthermore, higher ranked search results were considered more often than lower ranked results. However, not all the results for the most complex task were in line with expectations. These conflicting results can be explained by a lack of prior knowledge and the possible interference of prior attitudes.

0.062323727 = product of:
  0.18697117 = sum of:
    0.18697117 = weight(_text_:query in 2936) [ClassicSimilarity], result of:
      0.18697117 = score(doc=2936,freq=14.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.8151492 = fieldWeight in 2936, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=2936)
  0.33333334 = coord(1/3)

Abstract

Query reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of query reformulation during Web searching. This article reports results from a study in which we automatically classified the query-reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next query reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one query-reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45% of all query reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40% overall and higher than 70% for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance.

0.062323727 = product of:
  0.18697117 = sum of:
    0.18697117 = weight(_text_:query in 3643) [ClassicSimilarity], result of:
      0.18697117 = score(doc=3643,freq=14.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.8151492 = fieldWeight in 3643, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=3643)
  0.33333334 = coord(1/3)

Abstract

Although modern search systems require minimal skill for meeting simple information needs, most systems provide weak support for gaining advanced skill; hence, the goal of designing systems that guide searchers in developing expertise. Essential to developing such systems are a description of expert search behavior and an understanding of how it may be acquired. The present study contributes a detailed analysis of the query behavior of 10 students as they completed assigned exercises during a semester-long course on expert search. Detailed query logs were coded for three dimensions of query expression: the information structure searched, the type of query term used, and intent of the query with respect to specificity. Patterns of query formulation were found to evidence a progression of instruction, suggesting that the students gained knowledge of fundamental system-independent constructs that underlie expert search, and that domain-independent search expertise may be defined as the ability to use these constructs. Implications for system design are addressed.

0.062075913 = product of:
  0.18622774 = sum of:
    0.18622774 = weight(_text_:query in 3754) [ClassicSimilarity], result of:
      0.18622774 = score(doc=3754,freq=20.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.811908 = fieldWeight in 3754, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3754)
  0.33333334 = coord(1/3)

Abstract

Query reformulations can occur multiple times in a session, and queries observed in the same session tend to be related to each other. Due to the interdependent nature of queries in a session, it has been challenging to analyze query reformulation data while controlling for possible dependencies among queries. This study proposes a multilevel modeling approach in an attempt to analyze the effects of contextual factors and system features on types of query reformulation, as well as the relationship between types of query reformulation and search performance within a single research model. The results revealed that system features and users' educational background significantly influence users' query reformulation behaviors. Also, types of query reformulation had a significant impact on search performance. The main contribution of this study lies in that it adopted the multilevel modeling method to analyze query reformulation behavior while considering the nested structure of search session data. Multilevel analysis enables us to design an extensible research model to include both session-level and action-level factors, which provides a more extended understanding of the relationships among factors that influence query reformulation behavior and search performance. The multilevel modeling used in this study has practical implications for future query reformulation studies.

0.057700556 = product of:
  0.17310166 = sum of:
    0.17310166 = weight(_text_:query in 1300) [ClassicSimilarity], result of:
      0.17310166 = score(doc=1300,freq=12.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.75468147 = fieldWeight in 1300, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=1300)
  0.33333334 = coord(1/3)

Abstract

Children represent an increasing group of web users. Some of the key problems that hamper their search experience is their limited vocabulary, their difficulty in using the right keywords, and the inappropriateness of their general-purpose query suggestions. In this work, we propose a method that uses tags from social media to suggest queries related to children's topics. Concretely, we propose a simple yet effective approach to bias a random walk defined on a bipartite graph of web resources and tags through keywords that are more commonly used to describe resources for children. We evaluate our method using a large query log sample of queries submitted by children. We show that our method outperforms by a large margin the query suggestions of modern search engines and state-of-the art query suggestions based on random walks. We improve further the quality of the ranking by combining the score of the random walk with topical and language modeling features to emphasize even more the child-related aspects of the query suggestions.

0.055522382 = product of:
  0.16656715 = sum of:
    0.16656715 = weight(_text_:query in 2947) [ClassicSimilarity], result of:
      0.16656715 = score(doc=2947,freq=16.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.7261926 = fieldWeight in 2947, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2947)
  0.33333334 = coord(1/3)

Abstract

The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas.

0.055522382 = product of:
  0.16656715 = sum of:
    0.16656715 = weight(_text_:query in 5675) [ClassicSimilarity], result of:
      0.16656715 = score(doc=5675,freq=16.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.7261926 = fieldWeight in 5675, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5675)
  0.33333334 = coord(1/3)

Abstract

This article investigates how to improve the effectiveness of voice search systems. Earlier research found that participants employed voice search much less frequently than keyboard search. The main reasons that participants disliked voice search are system mistakes and the inability to modify queries. In keyboard search, query reformulation is facilitated by partial query modification, which is not supported by most of the current voice search systems. Consequently, users need to speak the complete query in voice search even with only minor changes. This article focuses on examining partial query modification during voice search through a Wizard of Oz user experiment. It examines if users would prefer partial query modification and how they perform it in voice search. Thirty-two participants participated in the experiment. Results indicated that when given the opportunity, the users performed more partial query modifications than complete queries. Common partial query modification strategies and patterns emerged from the experiment. The results can be used to improve the voice search system design and benefit the research community in general. System implications and future work were discussed.

0.054964356 = product of:
  0.16489306 = sum of:
    0.16489306 = weight(_text_:query in 5009) [ClassicSimilarity], result of:
      0.16489306 = score(doc=5009,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.71889395 = fieldWeight in 5009, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.109375 = fieldNorm(doc=5009)
  0.33333334 = coord(1/3)

0.054964356 = product of:
  0.16489306 = sum of:
    0.16489306 = weight(_text_:query in 4196) [ClassicSimilarity], result of:
      0.16489306 = score(doc=4196,freq=2.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.71889395 = fieldWeight in 4196, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.109375 = fieldNorm(doc=4196)
  0.33333334 = coord(1/3)

0.052673157 = product of:
  0.15801947 = sum of:
    0.15801947 = weight(_text_:query in 2676) [ClassicSimilarity], result of:
      0.15801947 = score(doc=2676,freq=10.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.68892676 = fieldWeight in 2676, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=2676)
  0.33333334 = coord(1/3)

Abstract

Finding information located somewhere on the WWW is an error-prone and frustrating task. The WebQuey system offers a powerful new method for searching the Web based on connectivity and content. We do this by examining links among the nodes returned in a keyword-based query. We then rank the nodes, giving the highest rank to the most highly connected nodes. By doing so, we are finding 'hot spots' on the Web that contain onformation germane to a user's query. WebQuery not only ranks and filters the results of a Web query, it also extends the result set beyond what the search engine retrieves, by finding 'interesting' sites that are hoghly connected to those sites returned by the original query. Even with WebQuery filtering and ranking query results, the result sets can be enourmous. So, wen need to visualize the returned information. We explore several techniques for visualizing this information - including cone trees, 2D graphs, 3D graphy, lists, and bullseyes - and discuss the criteria for using each of the techniques

0.052673157 = product of:
  0.15801947 = sum of:
    0.15801947 = weight(_text_:query in 4216) [ClassicSimilarity], result of:
      0.15801947 = score(doc=4216,freq=10.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.68892676 = fieldWeight in 4216, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.046875 = fieldNorm(doc=4216)
  0.33333334 = coord(1/3)

Abstract

Resource description extracted by query-sampling method can be applied to determine which database sources a certain query should be firstly sent to. In this paper, we propose a contextualized query-sampling method to extract the resources which are most relevant to up-to-date context. Practically, the proposed approach is adopted to personal crawler systems (the so-called focused crawlers), which can support the corresponding user's web navigation tasks in real-time. By taking into account the user context (e.g., intentions or interests), the crawler can build the queries to evaluate candidate information sources. As a result, we can discover semantic associations (i) between user context and the sources, and (ii) between all pairs of the sources. These associations are applied to rank the sources, and transform the queries for the other sources. For evaluating the performance of contextualized query sampling on 53 information sources, we compared the ranking lists recommended by the proposed method with user feedbacks (i.e., ideal ranks), and also computed the precision of discovered subsumptions as semantic associations between the sources.

0.051936433 = product of:
  0.1558093 = sum of:
    0.1558093 = weight(_text_:query in 200) [ClassicSimilarity], result of:
      0.1558093 = score(doc=200,freq=14.0), product of:
        0.22937049 = queryWeight, product of:
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.049352113 = queryNorm
        0.67929095 = fieldWeight in 200, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          4.6476326 = idf(docFreq=1151, maxDocs=44218)
          0.0390625 = fieldNorm(doc=200)
  0.33333334 = coord(1/3)

Abstract

This article compares search effectiveness when using query-based Internet search (via the Google search engine), directory-based search (via Yahoo), and phrase-based query reformulation-assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process and time was monitored as a function of search state. Results indicated directory-based search does not offer increased relevance over the query-based search (with or without query formulation assistance), and also takes longer. Query reformulation does significantly improve the relevance of the documents through which the user must trawl, particularly when the formulation of query terms is more difficult. However, the improvement in document relevance comes at the cost of increased search time, although this difference is quite small when the search is self-terminated. In addition, the advantage of the query reformulation seems to occur as a consequence of providing more discriminating terms rather than by increasing the length of queries