Search (64 results, page 1 of 4)

0.025785297 = product of:
  0.07735589 = sum of:
    0.041947264 = weight(_text_:applications in 5419) [ClassicSimilarity], result of:
      0.041947264 = score(doc=5419,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 5419, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5419)
    0.014968789 = weight(_text_:of in 5419) [ClassicSimilarity], result of:
      0.014968789 = score(doc=5419,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.24433708 = fieldWeight in 5419, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5419)
    0.020439833 = weight(_text_:systems in 5419) [ClassicSimilarity], result of:
      0.020439833 = score(doc=5419,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 5419, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5419)
  0.33333334 = coord(3/9)

Abstract

Understanding users' search behavior has largely relied on the information available from search engine logs, which provide limited information about the contextual factors affecting users' behavior. Consequently, questions such as how users' intentions, task goals, and substances of the users' tasks affect search behavior, as well as what triggers information needs, remain largely unanswered. We report an experiment in which naturalistic information search behavior was captured by analyzing 24/7 continuous recordings of information on participants' computer screens. Written task diaries describing the participants' tasks were collected and used as real-life task contexts for further categorization. All search tasks were extracted and classified under various task categories according to users' intentions, task goals, and substances of the tasks. We investigated the effect of different task categories on three behavioral factors: search efforts, content-triggers, and application context. Our results suggest four findings: (i) Search activity is integrally associated with the users' creative processes. The content users have seen prior to searching more often triggers search, and is used as a query, within creative tasks. (ii) Searching within intellectual and creative tasks is more time-intensive, while search activity occurring as a part of daily routine tasks is associated with more frequent searching within a search task. (iii) Searching is more often induced from utility applications in tasks demanding a degree of intellectual effort. (iv) Users' leisure information-seeking activity is occurring inherently within social media services or comes from social communication platforms. The implications of our findings for information access and management systems are discussed.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.11, S.1248-1261

0.018068818 = product of:
  0.054206453 = sum of:
    0.02049686 = weight(_text_:of in 297) [ClassicSimilarity], result of:
      0.02049686 = score(doc=297,freq=30.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.33457235 = fieldWeight in 297, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=297)
    0.020439833 = weight(_text_:systems in 297) [ClassicSimilarity], result of:
      0.020439833 = score(doc=297,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 297, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=297)
    0.013269759 = product of:
      0.026539518 = sum of:
        0.026539518 = weight(_text_:22 in 297) [ClassicSimilarity], result of:
          0.026539518 = score(doc=297,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.19345059 = fieldWeight in 297, 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=297)
      0.5 = coord(1/2)
  0.33333334 = coord(3/9)

Abstract

In information needs, seeking and use (INSU) research, individuals have most commonly been perceived as users (e.g., Kuhlthau, 1991; Dervin & Nilan, 1986; Dervin, 1989; Belkin, 1980). The concept user originates from the user of libraries and other information services and information systems. Over the years the scope of the concept has become wider and it is nowadays often understood in the sense of seekers of information (e.g., Wilson, 1981; Marchionini, 1995) and users of information (e.g., Streatfield, 1983). Nevertheless, the concept has remained ambiguous by being on the one hand universal and on the other hand extremely specific. The purpose of this paper is to map and evaluate views on people whose information behaviour has been in one way or another the core of our research area. The goal is to shed some light on various relationships between the different aspects of doers in INSU studies. The paper is inspired by Dervin's (1997) analysis of context where she identified among other themes the nature of subject by contrasting a `transcendental individual' with a `decentered subject', and Talja's (1997) presentation about constituting `information' and `user' from the discourse analytic viewpoint as opposed to the cognitive viewpoint. Instead of the metatheoretical approach applied by Dervin and Talja, a more concrete approach is valid in the present analysis where no direct arguments for or against the underlying metatheories are itemised. The focus is on doers in INSU studies leaving other, even closely-related concepts (i.e., information, information seeking, knowledge etc.), outside the scope of the paper.

Date

22. 3.2002 9:55:52

Source

Exploring the contexts of information behaviour: Proceedings of the 2nd International Conference on Research in Information Needs, Seeking and Use in Different Contexts, Sheffield, UK, 1998. Ed. by D.K. Wilson u. D.K. Allen

0.013971788 = product of:
  0.06287304 = sum of:
    0.021999538 = weight(_text_:of in 2091) [ClassicSimilarity], result of:
      0.021999538 = score(doc=2091,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3591007 = fieldWeight in 2091, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2091)
    0.040873505 = weight(_text_:software in 2091) [ClassicSimilarity], result of:
      0.040873505 = score(doc=2091,freq=2.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.2629875 = fieldWeight in 2091, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.046875 = fieldNorm(doc=2091)
  0.22222222 = coord(2/9)

Abstract

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

0.013324712 = product of:
  0.059961203 = sum of:
    0.019081537 = weight(_text_:of in 3699) [ClassicSimilarity], result of:
      0.019081537 = score(doc=3699,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.31146988 = fieldWeight in 3699, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3699)
    0.040879667 = weight(_text_:systems in 3699) [ClassicSimilarity], result of:
      0.040879667 = score(doc=3699,freq=8.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.339541 = fieldWeight in 3699, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3699)
  0.22222222 = coord(2/9)

Abstract

We report on a study that investigated the efficacy of four different interactive information retrieval (IIR) systems, each designed to support a specific information-seeking strategy (ISS). These systems were constructed using different combinations of IR techniques (i.e., combinations of different methods of representation, comparison, presentation and navigation), each of which was hypothesized to be well suited to support a specific ISS. We compared the performance of searchers in each such system, designated experimental, to an appropriate baseline system, which implemented the standard specified query and results list model of current state-of-the-art experimental and operational IR systems. Four within-subjects experiments were conducted for the purpose of this comparison. Results showed that each of the experimental systems was superior to its baseline system in supporting user performance for the specific ISS (that is, the information problem leading to that ISS) for which the system was designed. These results indicate that an IIR system, which intends to support more than one kind of ISS, should be designed within a framework which allows the use and combination of different IR support techniques for different ISSs.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1543-1563

0.013222162 = product of:
  0.059499726 = sum of:
    0.041947264 = weight(_text_:applications in 587) [ClassicSimilarity], result of:
      0.041947264 = score(doc=587,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2432066 = fieldWeight in 587, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0390625 = fieldNorm(doc=587)
    0.017552461 = weight(_text_:of in 587) [ClassicSimilarity], result of:
      0.017552461 = score(doc=587,freq=22.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.28651062 = fieldWeight in 587, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=587)
  0.22222222 = coord(2/9)

Abstract

Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.

Source

Journal of the American Society for Information Science and technology. 53(2002) no.8, S.617-630

0.012913696 = product of:
  0.05811163 = sum of:
    0.020956306 = weight(_text_:of in 3924) [ClassicSimilarity], result of:
      0.020956306 = score(doc=3924,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34207192 = fieldWeight in 3924, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.109375 = fieldNorm(doc=3924)
    0.037155323 = product of:
      0.074310645 = sum of:
        0.074310645 = weight(_text_:22 in 3924) [ClassicSimilarity], result of:
          0.074310645 = score(doc=3924,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.5416616 = fieldWeight in 3924, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=3924)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Source

Library and information science research. 22(2000) no.4, S.371-382

0.011078839 = product of:
  0.049854778 = sum of:
    0.0089812735 = weight(_text_:of in 2561) [ClassicSimilarity], result of:
      0.0089812735 = score(doc=2561,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.14660224 = fieldWeight in 2561, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2561)
    0.040873505 = weight(_text_:software in 2561) [ClassicSimilarity], result of:
      0.040873505 = score(doc=2561,freq=2.0), product of:
        0.15541996 = queryWeight, product of:
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.03917671 = queryNorm
        0.2629875 = fieldWeight in 2561, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.9671519 = idf(docFreq=2274, maxDocs=44218)
          0.046875 = fieldNorm(doc=2561)
  0.22222222 = coord(2/9)

Abstract

During the fall 2001 semester 15 first-year undergraduates were interviewed about their information-seeking behavior. Undergraduates completed a short-answer questionnaire, the Measure of Epistemological Reflection, measuring their epistemological beliefs and searched the Web and an online public access catalog using tasks from the Reflective Judgment Interview that assessed their reflective judgment level. Undergraduates talked aloud while searching digital environments about the decisions they were making about the information they encountered while transaction analyses software (Lotus ScreenCam) recorded both their search moves and their decision-making through verbal protocol analysis. Analyses included examining the relationship between undergraduates' epistemological beliefs and reflective judgment and how they searched for information in these digital environments. Results indicated that there was a relationship between epistemological beliefs and reflective judgment and information-seeking behavior. Undergraduates' at higher stages of epistemological development exhibited the ability to handle conflicting information sources and to recognize authoritative information sources.

0.010539032 = product of:
  0.047425643 = sum of:
    0.022897845 = weight(_text_:of in 5240) [ClassicSimilarity], result of:
      0.022897845 = score(doc=5240,freq=26.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.37376386 = fieldWeight in 5240, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=5240)
    0.0245278 = weight(_text_:systems in 5240) [ClassicSimilarity], result of:
      0.0245278 = score(doc=5240,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 5240, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=5240)
  0.22222222 = coord(2/9)

Abstract

In this issue we begin with the first of four parts of a five part series of papers by Spink, Wilson, Ford, Foster, and Ellis. Spink, et alia, in the first section of this report set forth the design of a project to test whether existing models of the information search process are appropriate for an environment of mediated successive searching which they believe characterizes much information seeking behavior. Their goal is to develop an integrated model of the process. Data were collected from 198 individuals, 87 in Texas and 111 in Sheffield in the U.K., with individuals with real information needs engaged in interaction with operational information retrieval systems by use of transaction logs, recordings of interactions with intermediaries, pre, and post search interviews, questionnaire responses, relevance judgments of retrieved text, and responses to a test of cognitive styles. Questionnaires were based upon the Kuhlthau model, the Saracevic model, the Ellis model, and incorporated a visual analog scale to avoid a consistency bias.

Source

Journal of the American Society for Information Science and Technology. 53(2002) no.9, S.695-703

0.010497608 = product of:
  0.047239237 = sum of:
    0.018332949 = weight(_text_:of in 600) [ClassicSimilarity], result of:
      0.018332949 = score(doc=600,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2992506 = fieldWeight in 600, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=600)
    0.02890629 = weight(_text_:systems in 600) [ClassicSimilarity], result of:
      0.02890629 = score(doc=600,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 600, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=600)
  0.22222222 = coord(2/9)

Abstract

Recent studies show that humans engage in multitasking behaviors as they seek and search information retrieval (IR) systems for information on more than one topic at the same time. For example, a Web search session by a single user may consist of searching on single topics or multitasking. Findings are presented from four separate studies of the prevalence of multitasking information seeking and searching by Web, IR system, and library users. Incidence of multitasking identified in the four different studies included: (1) users of the Excite Web search engine who completed a survey form, (2) Excite Web search engine users filtered from an Excite transaction log from 20 December 1999, (3) mediated on-line databases searches, and (4) academic library users. Findings include: (1) multitasking information seeking and searching is a common human behavior, (2) users may conduct information seeking and searching on related or unrelated topics, (3) Web or IR multitasking search sessions are longer than single topic sessions, (4) mean number of topics per Web search ranged of 1 to more than 10 topics with a mean of 2.11 topic changes per search session, and (4) many Web search topic changes were from hobbies to shopping and vice versa. A more complex model of human seeking and searching levels that incorporates multitasking information behaviors is presented, and a theoretical framework for human information coordinating behavior (HICB) is proposed. Multitasking information seeking and searching is developing as major research area that draws together IR and information seeking studies toward a focus on IR within the context of human information behavior. Implications for models of information seeking and searching, IR/Web systems design, and further research are discussed.

Source

Journal of the American Society for Information Science and technology. 53(2002) no.8, S.639-652

0.010142646 = product of:
  0.045641907 = sum of:
    0.016735615 = weight(_text_:of in 1713) [ClassicSimilarity], result of:
      0.016735615 = score(doc=1713,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27317715 = fieldWeight in 1713, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1713)
    0.02890629 = weight(_text_:systems in 1713) [ClassicSimilarity], result of:
      0.02890629 = score(doc=1713,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 1713, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1713)
  0.22222222 = coord(2/9)

Abstract

User studies demonstrate that nondomain experts do not use the same information-seeking strategies as domain experts. Because of the transformation of integrated library systems into Information Gateways in the late 1990s, both nondomain experts and domain experts have had available to them the wide range of information-seeking strategies in a single system. This article describes the results of a study to answer three research questions: (1) do nondomain experts enlist the strategies of domain experts? (2) if they do, how did they learn about these strategies? and (3) are they successful using them? Interviews, audio recordings, screen captures, and observations were used to gather data from 14 undergraduate students who searched an academic library's Information Gateway. The few times that the undergraduates in this study enlisted search strategies that were characteristic of domain experts, it usually took perseverance, trial-and-error, serendipity, or a combination of all three for them to find useful information. Although this study's results provide no compelling reasons for systems to support features that make domain-expert strategies possible, there is need for system features that scaffold nondomain experts from their usual strategies to the strategies characteristic of domain experts.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.9, S.836-854

0.009951797 = product of:
  0.044783086 = sum of:
    0.015876798 = weight(_text_:of in 5410) [ClassicSimilarity], result of:
      0.015876798 = score(doc=5410,freq=18.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.25915858 = fieldWeight in 5410, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5410)
    0.02890629 = weight(_text_:systems in 5410) [ClassicSimilarity], result of:
      0.02890629 = score(doc=5410,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.24009174 = fieldWeight in 5410, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5410)
  0.22222222 = coord(2/9)

Abstract

It is a frequently seen scenario that when people are not familiar with their search topics, they use a simple keyword search, which leads to a large amount of search results in multiple pages. This makes it difficult for users to pick relevant documents, especially given that they are not knowledgeable of the topics. To explore how systems can better help users find relevant documents from search results, the current research analyzed document selection behaviors of users with different levels of domain knowledge (DK). Data were collected in a laboratory study with 35 participants each searching on four tasks in the genomics domain. The results show that users with high and low DK levels selected different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the viewed documents than those low in DK did. Users with low DK tended to select documents ranking toward the top of the search result lists, and those with high in DK tended to also select documents ranking down the search result lists. The findings help design search systems that can personalize search results to users with different levels of DK.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.11, S.1236-1247

0.009684435 = product of:
  0.04357996 = sum of:
    0.019052157 = weight(_text_:of in 2936) [ClassicSimilarity], result of:
      0.019052157 = score(doc=2936,freq=18.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3109903 = fieldWeight in 2936, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2936)
    0.0245278 = weight(_text_:systems in 2936) [ClassicSimilarity], result of:
      0.0245278 = score(doc=2936,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 2936, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=2936)
  0.22222222 = coord(2/9)

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.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.7, S.1358-1371

0.009652025 = product of:
  0.043434113 = sum of:
    0.014818345 = weight(_text_:of in 6566) [ClassicSimilarity], result of:
      0.014818345 = score(doc=6566,freq=8.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.24188137 = fieldWeight in 6566, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6566)
    0.028615767 = weight(_text_:systems in 6566) [ClassicSimilarity], result of:
      0.028615767 = score(doc=6566,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.23767869 = fieldWeight in 6566, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6566)
  0.22222222 = coord(2/9)

Abstract

This study applied the Biglan model of disciplinary differences to the information-seeking behavior patterns of 5,175 undergraduates responding to questions on the College Student Experiences Questionnaire (CSEQ). The Biglan model categorizes academic disciplines along three dimensions: (1) hard-soft, (2) pure-applied, and (3) life-nonlife systems. Using t-tests, this model proved to be valid for distinguishing differences in undergraduates' information-seeking behavior patterns among various academic disciplines. The results indicate that the Biglan model has implications for the redesign of academic library services and use as a valid theoretical framework for future library and information science research.

Source

Journal of the American Society for Information Science and technology. 53(2002) no.8, S.631-638

0.009184495 = product of:
  0.041330226 = sum of:
    0.016802425 = weight(_text_:of in 4472) [ClassicSimilarity], result of:
      0.016802425 = score(doc=4472,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2742677 = fieldWeight in 4472, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4472)
    0.0245278 = weight(_text_:systems in 4472) [ClassicSimilarity], result of:
      0.0245278 = score(doc=4472,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.2037246 = fieldWeight in 4472, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=4472)
  0.22222222 = coord(2/9)

Abstract

This paper reports the results of a study funded by the Arts and Humanities Research Board which sought to investigate links between Web search strategies and retrieval effectiveness. A total of 68 students, enrolled on masters programmes in librarianship, information management and information systems, searched for two topics using the AltaVista search engine. Logs of the resultant 341 queries, along with relevance judgements for over 4,000 retrieved items, were analysed using factor analysis and regression. The differing but complementary types and strengths of evidence produced by these two forms of analysis are discussed and presented. Retrieval effectiveness was associated positively with best-match searching and negatively with Boolean searching. The implications of these findings for Web searching are discussed.

Source

Journal of documentation. 58(2002) no.1, S.30-48

0.008942596 = product of:
  0.04024168 = sum of:
    0.019801848 = weight(_text_:of in 3608) [ClassicSimilarity], result of:
      0.019801848 = score(doc=3608,freq=28.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.32322758 = fieldWeight in 3608, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3608)
    0.020439833 = weight(_text_:systems in 3608) [ClassicSimilarity], result of:
      0.020439833 = score(doc=3608,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 3608, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3608)
  0.22222222 = coord(2/9)

Abstract

Purpose - The purpose of this paper is to examine changes in relevance assessments, specifically the selection of relevance criteria by subjects as they move through the information search process. Design/methodology/approach - The paper examines the relevance criteria choices of 39 subjects in relation to search stage. Subjects were assigned a specific search task in a controlled test. Statistics were collected and analyzed using descriptive statistics and the chi-square goodness-of-fit tests. Findings - The statistically significant findings identified a number of commonly reported relevance criteria, which varied over an information search process for relevant and partially relevant judgments. These results provide statistical confirmations of previous studies, and extend these findings identifying specific criteria for both relevant and partially relevant judgments. Research limitations/implications - The study only examines a short duration search process and since the convenience sample of subjects were from similar backgrounds and were assigned similar tasks, the study did not explicitly examine the impact of contextual factors such as user experience, background or task in relation to relevance criteria choices. Practical implications - The paper has implications for the development of search systems which are adaptive and recognize the cognitive changes which occur during the information search process. Examining and identifying relevance criteria beyond topicality and the importance of those criteria to a user can help in the generation of better search queries. Originality/value - The paper adds more rigorous statistical analysis to the study of relevance criteria and the information search process.

Source

Journal of documentation. 65(2009) no.5, S.719-744

0.008616175 = product of:
  0.038772784 = sum of:
    0.018332949 = weight(_text_:of in 198) [ClassicSimilarity], result of:
      0.018332949 = score(doc=198,freq=24.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2992506 = fieldWeight in 198, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=198)
    0.020439833 = weight(_text_:systems in 198) [ClassicSimilarity], result of:
      0.020439833 = score(doc=198,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 198, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=198)
  0.22222222 = coord(2/9)

Abstract

Conventional wisdom holds that queries to information retrieval systems will yield more relevant results if they contain multiple topic-related terms and use Boolean and phrase operators to enhance interpretation. Although studies have shown that the users of Web-based search engines typically enter short, term-based queries and rarely use search operators, little information exists concerning the effects of term and operator usage on the relevancy of search results. In this study, search engine users formulated queries on eight search topics. Each query was submitted to the user-specified search engine, and relevancy ratings for the retrieved pages were assigned. Expert-formulated queries were also submitted and provided a basis for comparing relevancy ratings across search engines. Data analysis based on our research model of the term and operator factors affecting relevancy was then conducted. The results show that the difference in the number of terms between expert and nonexpert searches, the percentage of matching terms between those searches, and the erroneous use of nonsupported operators in nonexpert searches explain most of the variation in the relevancy of search results. These findings highlight the need for designing search engine interfaces that provide greater support in the areas of term selection and operator usage

Source

Journal of the American Society for Information Science and technology. 53(2002) no.2, S.95-108

0.0084427325 = product of:
  0.037992295 = sum of:
    0.017552461 = weight(_text_:of in 2423) [ClassicSimilarity], result of:
      0.017552461 = score(doc=2423,freq=22.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.28651062 = fieldWeight in 2423, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2423)
    0.020439833 = weight(_text_:systems in 2423) [ClassicSimilarity], result of:
      0.020439833 = score(doc=2423,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 2423, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2423)
  0.22222222 = coord(2/9)

Abstract

Search statements in this revision process can be viewed as a 'move' in the overall search strategy. Very little is known about how end users develop and revise their search strategies. A study was conducted to analyse the moves made in 244 data base searches conducted by 26 medical students at the University of North Carolina at Chapel Hill. Students search INQUIRER, a data base of facts and concepts in microbiology. The searches were conducted during a 3-week period in spring 1990 and were recorded by the INQUIRER system. Each search statement was categorised, using Fidel's online searching moves (S. Online review 9(1985) S.61-74) and Bates' search tactics (s. JASIS 30(1979) S.205-214). Further analyses indicated that the most common moves were Browse/Specity, Select Exhaust, Intersect, and Vary, and that selection of moves varied by student and by problem. Analysis of search tactics (combinations of moves) identified 5 common search approaches. The results of this study have implcations for future research on search behaviours, for thedesign of system interfaces and data base structures, and for the training of end users

Source

ASIS'91: systems understanding people. Proc. of the 54th Annual Meeting of the ASIS, vol.28, Washington, DC, 27.-31.10.1991. Ed.: J.-M. Griffiths

0.00826121 = product of:
  0.037175447 = sum of:
    0.016735615 = weight(_text_:of in 1081) [ClassicSimilarity], result of:
      0.016735615 = score(doc=1081,freq=20.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27317715 = fieldWeight in 1081, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1081)
    0.020439833 = weight(_text_:systems in 1081) [ClassicSimilarity], result of:
      0.020439833 = score(doc=1081,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 1081, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1081)
  0.22222222 = coord(2/9)

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.

0.007868583 = product of:
  0.035408624 = sum of:
    0.014968789 = weight(_text_:of in 4097) [ClassicSimilarity], result of:
      0.014968789 = score(doc=4097,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.24433708 = fieldWeight in 4097, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4097)
    0.020439833 = weight(_text_:systems in 4097) [ClassicSimilarity], result of:
      0.020439833 = score(doc=4097,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 4097, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4097)
  0.22222222 = coord(2/9)

Abstract

Although many studies have identified search tactics, few studies have explored tactic transitions. This study investigated the transitions of search tactics during the Web-based search process. Bringing their own 60 search tasks, 31 participants, representing the general public with different demographic characteristics, participated in the study. Data collected from search logs and verbal protocols were analyzed by applying both qualitative and quantitative methods. The findings of this study show that participants exhibited some unique Web search tactics. They overwhelmingly employed accessing and evaluating tactics; they used fewer tactics related to modifying search statements, monitoring the search process, organizing search results, and learning system features. The contributing factors behind applying most and least frequently employed search tactics are in relation to users' efforts, trust in information retrieval (IR) systems, preference, experience, and knowledge as well as limitation of the system design. A matrix of search-tactic transitions was created to show the probabilities of transitions from one tactic to another. By applying fifth-order Markov chain, the results also presented the most common search strategies representing patterns of tactic transition occurring at the beginning, middle, and ending phases within one search session. The results of this study generated detailed and useful guidance for IR system design to support the most frequently applied tactics and transitions, to reduce unnecessary transitions, and support transitions at different phases.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2188-2205

0.006772034 = product of:
  0.030474152 = sum of:
    0.019858345 = weight(_text_:of in 296) [ClassicSimilarity], result of:
      0.019858345 = score(doc=296,freq=44.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.3241498 = fieldWeight in 296, product of:
          6.6332498 = tf(freq=44.0), with freq of:
            44.0 = termFreq=44.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03125 = fieldNorm(doc=296)
    0.010615807 = product of:
      0.021231614 = sum of:
        0.021231614 = weight(_text_:22 in 296) [ClassicSimilarity], result of:
          0.021231614 = score(doc=296,freq=2.0), product of:
            0.13719016 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03917671 = queryNorm
            0.15476047 = fieldWeight in 296, 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=296)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Previous empirical studies of searcher behaviour have drawn attention to a wide variety of factors that affect performance; for instance, the display of retrieved results can alter search strategies (Allen 1991, 1994), the information need type influences search behaviour, (Elkerton et al 1984, Marchionini 1995); while the task complexity, reflected in the information need can affect user's search behaviour (Large et al 1994). Furthermore, information source selection (Bassilli 1977), and the user's model of the system and domain impact on the search process (Michel 1994); while motivation (Solomon 1993, Jacobsen et al 1992) and the importance of the information need (Wendt 1969) also influence search duration and the effort a user will employ. Rouse and Rouse (1984) in a review of empirical studies, summarise a wide variety of variables that can effect searching behaviour, including payoff, costs of searching, resource available, amount of information sought, characteristics of the data and conflicts between documents. It appears that user behaviour is inconsistent in the search strategies adopted even for the same search need and system (Davidson 1977, Iivonen 1995). Theories of searcher behaviour have been proposed that provide explanations of aspects of end-user behaviour, such as the evolution of the user's information need and the problems of articulating a query, [Bates (1979, 1989), Markey and Atherton 1978], effective search strategies in browsing and goal directed searches [Marchionini 1995, Belkin (1987, 1993)], the linguistic problem of matching search terms with indexing terms or content of target documents through an expert intermediary (Ingwersen 1982) or cognitive aspects of IR (Kulthau 1984, Ingwersen 1996).

Date

22. 3.2002 9:54:13

Source

Exploring the contexts of information behaviour: Proceedings of the 2nd International Conference on Research in Information Needs, Seeking and Use in Different Contexts, Sheffield, UK, 1998. Ed. by D.K. Wilson u. D.K. Allen