Search (3 results, page 1 of 1)

  • × theme_ss:"Suchtaktik"
  • × author_ss:"Shah, C."
  1. González-Ibáñez, R.; Shah, C.; White, R.W.: Capturing 'Collabportunities' : a method to evaluate collaboration opportunities in information search using pseudocollaboration (2015) 0.04
    0.037090525 = product of:
      0.11127157 = sum of:
        0.11127157 = weight(_text_:search in 2167) [ClassicSimilarity], result of:
          0.11127157 = score(doc=2167,freq=22.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.6368113 = fieldWeight in 2167, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2167)
      0.33333334 = coord(1/3)
    
    Abstract
    In explicit collaborative search, two or more individuals coordinate their efforts toward a shared goal. Every day, Internet users with similar information needs have the potential to collaborate. However, online search is typically performed in solitude. Existing search systems do not promote explicit collaborations, and collaboration opportunities (collabportunities) are missed. In this article, we describe a method to evaluate the feasibility of transforming these collabportunities into recommendations for explicit collaboration. We developed a technique called pseudocollaboration to evaluate the benefits and costs of collabportunities through simulations. We evaluate the performance of our method using three data sets: (a) data from single users' search sessions, (b) data with collaborative search sessions between pairs of searchers, and (c) logs from a large-scale search engine with search sessions of thousands of searchers. Our results establish when and how collabportunities would significantly help or hinder the search process versus searches conducted individually. The method that we describe has implications for the design and implementation of recommendation systems for explicit collaboration. It also connects system-mediated and user-mediated collaborative search, whereby the system evaluates the likely benefits of collaborating for a search task and helps searchers make more informed decisions on initiating and executing such a collaboration.
  2. Shah, C.; Marchionini, G.: Awareness in collaborative information seeking (2010) 0.02
    0.023243874 = product of:
      0.06973162 = sum of:
        0.06973162 = weight(_text_:search in 4082) [ClassicSimilarity], result of:
          0.06973162 = score(doc=4082,freq=6.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.39907667 = fieldWeight in 4082, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.046875 = fieldNorm(doc=4082)
      0.33333334 = coord(1/3)
    
    Abstract
    Support for explicit collaboration in information-seeking activities is increasingly recognized as a desideratum for search systems. Several tools have emerged recently that help groups of people with the same information-seeking goals to work together. Many issues for these collaborative information-seeking (CIS) environments remain understudied. The authors identified awareness as one of these issues in CIS, and they presented a user study that involved 42 pairs of participants, who worked in collaboration over 2 sessions with 3 instances of the authors' CIS system for exploratory search. They showed that while having awareness of personal actions and history is important for exploratory search tasks spanning multiple sessions, support for group awareness is even more significant for effective collaboration. In addition, they showed that support for such group awareness can be provided without compromising usability or introducing additional load on the users.
  3. Wang, Y.; Shah, C.: Authentic versus synthetic : an investigation of the influences of study settings and task configurations on search behaviors (2022) 0.02
    0.015815454 = product of:
      0.04744636 = sum of:
        0.04744636 = weight(_text_:search in 495) [ClassicSimilarity], result of:
          0.04744636 = score(doc=495,freq=4.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.27153727 = fieldWeight in 495, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=495)
      0.33333334 = coord(1/3)
    
    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.