Search (3 results, page 1 of 1)

  • × theme_ss:"Suchtaktik"
  • × author_ss:"Spink, A."
  1. Spink, A.; Ozmutlu, H.C.; Ozmutlu, S.: Multitasking information seeking and searching processes (2002) 0.01
    0.009625921 = product of:
      0.019251842 = sum of:
        0.019251842 = product of:
          0.038503684 = sum of:
            0.038503684 = weight(_text_:systems in 600) [ClassicSimilarity], result of:
              0.038503684 = score(doc=600,freq=4.0), product of:
                0.16037072 = queryWeight, product of:
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.052184064 = 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)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
  2. Spink, A.; Wilson, T.D.; Ford, N.; Foster, A.; Ellis, D.: Information seeking and mediated searching : Part 1: theoretical framework and research design (2002) 0.01
    0.008167865 = product of:
      0.01633573 = sum of:
        0.01633573 = product of:
          0.03267146 = sum of:
            0.03267146 = weight(_text_:systems in 5240) [ClassicSimilarity], result of:
              0.03267146 = score(doc=5240,freq=2.0), product of:
                0.16037072 = queryWeight, product of:
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.052184064 = 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)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
  3. Jansen, B.J.; Booth, D.L.; Spink, A.: Patterns of query reformulation during Web searching (2009) 0.01
    0.008167865 = product of:
      0.01633573 = sum of:
        0.01633573 = product of:
          0.03267146 = sum of:
            0.03267146 = weight(_text_:systems in 2936) [ClassicSimilarity], result of:
              0.03267146 = score(doc=2936,freq=2.0), product of:
                0.16037072 = queryWeight, product of:
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.052184064 = 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)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.