Search (3 results, page 1 of 1)

0.044422872 = product of:
  0.066634305 = sum of:
    0.037957087 = weight(_text_:search in 647) [ClassicSimilarity], result of:
      0.037957087 = score(doc=647,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.21722981 = fieldWeight in 647, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=647)
    0.028677218 = product of:
      0.057354435 = sum of:
        0.057354435 = weight(_text_:engines in 647) [ClassicSimilarity], result of:
          0.057354435 = score(doc=647,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.22454272 = fieldWeight in 647, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.03125 = fieldNorm(doc=647)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality.

0.03438644 = product of:
  0.051579658 = sum of:
    0.037957087 = weight(_text_:search in 642) [ClassicSimilarity], result of:
      0.037957087 = score(doc=642,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.21722981 = fieldWeight in 642, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=642)
    0.013622572 = product of:
      0.027245143 = sum of:
        0.027245143 = weight(_text_:22 in 642) [ClassicSimilarity], result of:
          0.027245143 = score(doc=642,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.15476047 = fieldWeight in 642, 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=642)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Information retrieval (IR) research has developed considerably since the 1950's to include consideration of more cognitive, interactive and iterative processes during the interaction between humans and IR or Web systems (Ingwersen, 1992, 1996). Interactive search sessions by humans with IR systems have been depicted as interactive IR models (Saracevic, 1997). Human-IR system interaction is also modeled as taking place within the context of broader human information behavior (HIB) processes (Spink et al., 2002). Research into the human or cognitive (user modeling) aspects of IR is a growing body of research on user interactivity, task performance and measures for observing user interactivity. The task context and situational characteristics of users' searches and evaluation have also been identified as key elements in a user's interaction with an IR system (Cool and Spink, 2002; Vakkari, 2003). Major theorized interactive IR models have been proposed relating to the single search episode, including Ingwersen's (1992,1996) Cognitive Model of IR Interaction, Belkin et al.'s (1995) Episodic Interaction Model, and Saracevic's (1996,1997) Stratified Model of IR Interaction. In this chapter we examine Saracevic's Stratified Model of IR Interaction and extend the model within the framework of cognitive IR (CIR) to depict CIR as a multitasking process. This chapter provides a new direction for CIR research by conceptualizing IR with a multitasking context. The next section of the chapter defines the concept of multitasking in the cognitive sciences and Section 3 discusses the emerging understanding of multitasking information behavior. In Section 4, cognitive IR is depicted within a multitasking framework using Saracevic's (1996, 1997) Stratified Model of IR Interaction. In Section 5, we link information searching and seeking models together, via Saracevic's Stratified Model of IR Interaction, but starting with a unitask model of HIB. We begin to model multitasking in cognitive IR in Section 6. In Sections 7 and 8, we increase the complexity of our developing multitasking model of cognitive IR by adding coordinating mechanisms, including feedback loops. Finally, in Section 9, we conclude the chapter and indicate future directions for further research.

Date

19. 1.2007 12:55:22

0.012652363 = product of:
  0.037957087 = sum of:
    0.037957087 = weight(_text_:search in 2389) [ClassicSimilarity], result of:
      0.037957087 = score(doc=2389,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.21722981 = fieldWeight in 2389, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=2389)
  0.33333334 = coord(1/3)

Abstract

This is the second part of a two-part special topic JASIST issue an information seeking. The first part presented papers an the topics of health information seeking and everyday life information seeking or ELIS (i.e., information seeking outside of work or school). This second issue presents papers an the topics of information retrieval and information seeking in industry environments. Information retrieval involves a specific kind of information seeking, as the user is in direct contact with an information interface and with potential sources of information from the system's database. The user conducts the search using various strategies, tactics, etc., but there is also the possibility that information processes will occur resulting in a change in the way the user thinks about the topic of the search. If this occurs, the user is, in effect, using the found data, turning it into an informational element of some kind. Such processes can be facilitated in the design of the information retrieval system. Information seeking in industry environments takes up more and more of our working day. Even companies producing industrial products are in fact mainly producing informational elements of some kind, often for the purpose of making decisions or as starting positions for further information seeking. While there may be company mechanisms in place to aid such information seeking, and to make it more efficient, if better information seeking structures were in place, not only would workers waste less time in informational pursuits, but they would also find things, discover new processes, etc., that would benefit the corporation's bottom line. In Figure l, we plot the six papers in this issue an an information behavior continuum, following a taxonomy of information behavior terms from Spink and Cole (2001). Information Behavior is a broad term covering all aspects of information seeking, including passive or undetermined information behavior. Information-Seeking Behavior is usually thought of as active or conscious information behavior. Information-Searching Behavior describes the interactive elements between a user and an information system. Information-Use Behavior is about the user's acquisition and incorporation of data in some kind of information process. This leads to the production of information, but also back to the broad range of Information Behavior in the first part of the continuum. Though we plot all papers in this issue along this continuum, they take into account more than their general framework. The three information retrieval reports veer from the traditional information-searching approach of usersystem interaction, while the three industry environment articles veer from the traditional information-seeking approach of specific context information-seeking studies.