Search (61 results, page 1 of 4)

  • × author_ss:"Spink, A."
  1. Reneker, M.; Jacobson, A.; Wargo, L.; Spink, A.: Information environment of a military university campus : an exploratory study (1999) 0.04
    0.039331026 = product of:
      0.07866205 = sum of:
        0.014731225 = weight(_text_:for in 6704) [ClassicSimilarity], result of:
          0.014731225 = score(doc=6704,freq=8.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.16595288 = fieldWeight in 6704, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.03125 = fieldNorm(doc=6704)
        0.06393083 = weight(_text_:computing in 6704) [ClassicSimilarity], result of:
          0.06393083 = score(doc=6704,freq=2.0), product of:
            0.26151994 = queryWeight, product of:
              5.5314693 = idf(docFreq=475, maxDocs=44218)
              0.047278564 = queryNorm
            0.24445872 = fieldWeight in 6704, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5314693 = idf(docFreq=475, maxDocs=44218)
              0.03125 = fieldNorm(doc=6704)
      0.5 = coord(2/4)
    
    Abstract
    The Naval Postgraduate School (NPS) is a military university educating officers from the United States and 40 foreign countries. To investigate the NPS information environment a large study obtained data on the range of information needs and behaviors of NPS personnel. The specific aim of the study was to supply organizational units with qualitative data specific to their client base, enabling them to improve campus systems and information services. Facilitators from the NPS Organizational Support Division conducted eighteen (18) focus groups during Spring Quarter 1998. Transcribed focus group sessions were analyzed using NUDIST software to identify key issues and results emerging from the data set. Categories of participants' information needs were identified, including an analysis of key information issues across the NPS campus. Use of Internet resources, other trusted individuals, and electronic indexes and abstracts ranked high among information sources used by NPS personnel. A picture emerges of a campus information environment poorly understood by the academic community. The three groups (students, staff and faculty) articulated different concerns and look to different sources to satisfy their information needs. Participants' information seeking problems centered on: (1) housing, registration and scheduling, computing and the quality of information available on the campus computer network, (2) an inability to easily disseminate information quickly to an appropriate campus audience, and (3) training in new information access technologies, and (4) the general lack of awareness of library resources and services. The paper discusses a method for more effectively disseminating information throughout the campus. Implications for the development of information seeking models and a model of the NPS information environment are discussed
    Series
    Proceedings of the American Society for Information Science; vol.36
    Source
    Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods
  2. Spink, A.; Cole, C.: New directions in cognitive information retrieval : introduction (2005) 0.04
    0.039331026 = product of:
      0.07866205 = sum of:
        0.014731225 = weight(_text_:for in 647) [ClassicSimilarity], result of:
          0.014731225 = score(doc=647,freq=8.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.16595288 = fieldWeight in 647, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.03125 = fieldNorm(doc=647)
        0.06393083 = weight(_text_:computing in 647) [ClassicSimilarity], result of:
          0.06393083 = score(doc=647,freq=2.0), product of:
            0.26151994 = queryWeight, product of:
              5.5314693 = idf(docFreq=475, maxDocs=44218)
              0.047278564 = queryNorm
            0.24445872 = fieldWeight in 647, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5314693 = idf(docFreq=475, maxDocs=44218)
              0.03125 = fieldNorm(doc=647)
      0.5 = coord(2/4)
    
    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.
  3. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.02
    0.021960896 = product of:
      0.04392179 = sum of:
        0.024705013 = weight(_text_:for in 2742) [ClassicSimilarity], result of:
          0.024705013 = score(doc=2742,freq=10.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.27831143 = fieldWeight in 2742, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.046875 = fieldNorm(doc=2742)
        0.019216778 = product of:
          0.038433556 = sum of:
            0.038433556 = weight(_text_:22 in 2742) [ClassicSimilarity], result of:
              0.038433556 = score(doc=2742,freq=2.0), product of:
                0.16556148 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.047278564 = queryNorm
                0.23214069 = fieldWeight in 2742, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2742)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.
    Date
    22. 3.2009 17:49:11
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.557-570
  4. Spink, A.; Cole, C.: ¬A multitasking framework for cognitive information retrieval (2005) 0.01
    0.013771205 = product of:
      0.02754241 = sum of:
        0.014731225 = weight(_text_:for in 642) [ClassicSimilarity], result of:
          0.014731225 = score(doc=642,freq=8.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.16595288 = fieldWeight in 642, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.03125 = fieldNorm(doc=642)
        0.012811186 = product of:
          0.025622372 = sum of:
            0.025622372 = weight(_text_:22 in 642) [ClassicSimilarity], result of:
              0.025622372 = score(doc=642,freq=2.0), product of:
                0.16556148 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.047278564 = 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)
      0.5 = coord(2/4)
    
    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
  5. Jansen, B.J.; Booth, D.L.; Spink, A.: Patterns of query reformulation during Web searching (2009) 0.01
    0.0067657465 = product of:
      0.027062986 = sum of:
        0.027062986 = weight(_text_:for in 2936) [ClassicSimilarity], result of:
          0.027062986 = score(doc=2936,freq=12.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.3048749 = fieldWeight in 2936, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.046875 = fieldNorm(doc=2936)
      0.25 = coord(1/4)
    
    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
  6. Tjondronegoro, D.; Spink, A.; Jansen, B.J.: ¬A study and comparison of multimedia Web searching : 1997-2006 (2009) 0.01
    0.005638122 = product of:
      0.022552488 = sum of:
        0.022552488 = weight(_text_:for in 3090) [ClassicSimilarity], result of:
          0.022552488 = score(doc=3090,freq=12.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.2540624 = fieldWeight in 3090, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3090)
      0.25 = coord(1/4)
    
    Abstract
    Searching for multimedia is an important activity for users of Web search engines. Studying user's interactions with Web search engine multimedia buttons, including image, audio, and video, is important for the development of multimedia Web search systems. This article provides results from a Weblog analysis study of multimedia Web searching by Dogpile users in 2006. The study analyzes the (a) duration, size, and structure of Web search queries and sessions; (b) user demographics; (c) most popular multimedia Web searching terms; and (d) use of advanced Web search techniques including Boolean and natural language. The current study findings are compared with results from previous multimedia Web searching studies. The key findings are: (a) Since 1997, image search consistently is the dominant media type searched followed by audio and video; (b) multimedia search duration is still short (>50% of searching episodes are <1 min), using few search terms; (c) many multimedia searches are for information about people, especially in audio search; and (d) multimedia search has begun to shift from entertainment to other categories such as medical, sports, and technology (based on the most repeated terms). Implications for design of Web multimedia search engines are discussed.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.9, S.1756-1768
  7. Spink, A.; Goodrum, A.; Robins, D.: Elicitation behavior during mediated information retrieval (1998) 0.01
    0.0055814567 = product of:
      0.022325827 = sum of:
        0.022325827 = weight(_text_:for in 3265) [ClassicSimilarity], result of:
          0.022325827 = score(doc=3265,freq=6.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.25150898 = fieldWeight in 3265, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3265)
      0.25 = coord(1/4)
    
    Abstract
    Considers what elicitation or requests for information search intermediaries make of users with information requests during an information retrieval interaction - including prior to and during an information retrieval interaction - and for what purpose. Reports a study of elicitations during 40 mediated information retrieval interactions. Identifies a total of 1.557 search intermediary elicitations within 15 purpose categories. The elicitation purposes of search intermediaries included requests for information on search terms and strategies, database selection, search procedures, system's outputs and relevance of retrieved items, and users' knowledge and previous information seeking. Investigates the transition sequences from 1 type of search intermediary elicitation to another. Compares these findings with results from a study of end user questions
  8. He, S.; Spink, A.: ¬A comparison of foreign authorship distribution in JASIST and the Journal of Documentation (2002) 0.01
    0.0055814567 = product of:
      0.022325827 = sum of:
        0.022325827 = weight(_text_:for in 5230) [ClassicSimilarity], result of:
          0.022325827 = score(doc=5230,freq=6.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.25150898 = fieldWeight in 5230, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5230)
      0.25 = coord(1/4)
    
    Abstract
    He and Spink count the first authors in JASIST and JDoc from 1950 to 1999 whose affiliation is outside the country of origin of each publication and record the time period and the author's geographic location. Foreign authorship in JASIST increased nearly four fold from 1995 to 1999 and the number of represented locations 3.6 times while in the same time period JDoc's foreign authorship doubled and foreign locations increased four fold. The largest foreign location for JDoc is the USA and the largest foreign location for JASIST is the UK. Canada is second on both lists.
    Source
    Journal of the American Society for Information Science and Technology. 53(2002) no.11, S.953-959
  9. Wolfram, D.; Spink, A.; Jansen, B.J.; Saracevic, T.: Vox populi : the public searching of the Web (2001) 0.01
    0.0055242092 = product of:
      0.022096837 = sum of:
        0.022096837 = weight(_text_:for in 6949) [ClassicSimilarity], result of:
          0.022096837 = score(doc=6949,freq=2.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.24892932 = fieldWeight in 6949, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.09375 = fieldNorm(doc=6949)
      0.25 = coord(1/4)
    
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.12, S.1073-1074
  10. Spink, A.: Study of interactive feedback during mediated information retrieval (1997) 0.01
    0.0052082743 = product of:
      0.020833097 = sum of:
        0.020833097 = weight(_text_:for in 158) [ClassicSimilarity], result of:
          0.020833097 = score(doc=158,freq=4.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.23469281 = fieldWeight in 158, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0625 = fieldNorm(doc=158)
      0.25 = coord(1/4)
    
    Abstract
    Reports results from a study exploring the information retrieval and types of interactive feedback during mediated information retrieval. Identifies 5 different types of interactive feedback, extending the interactive information retrieval model to include relevance, magnitude, and strategy interactive feedback. Discusses implications for further research, investigating the nature and model of interactive feedback in information retrieval
    Source
    Journal of the American Society for Information Science. 48(1997) no.5, S.382-394
  11. Goodrum, A.; Spink, A.: Visual information seeking : a study of image queries on the world wide web (1999) 0.01
    0.0052082743 = product of:
      0.020833097 = sum of:
        0.020833097 = weight(_text_:for in 6678) [ClassicSimilarity], result of:
          0.020833097 = score(doc=6678,freq=16.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.23469281 = fieldWeight in 6678, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.03125 = fieldNorm(doc=6678)
      0.25 = coord(1/4)
    
    Abstract
    A growing body of research is beginning to explore the information-seeking behavior of Web users. The vast majority of these studies have concentrated on the area of textual information retrieval (IR). Little research has examined how people search for non-textual information on the Internet, and few large-scale studies have investigated visual information-seeking behavior with Web search engines. This study examined visual information needs as expressed in users' Web image queries. The data set examined consisted of 1,025,908 sequential queries from 211,058 users of EXCITE, a major Internet search service. Twenty-eight (28) terms were used to identify queries for both still and moving images, resulting in a subset of 33,149 image queries by 9,855 users. We provide data on: (1) image queries -- the number of queries and the number of search terms per user, (2) image search sessions -- the number of queries per user, modifications made to subsequent queries in a session, and (3) image terms -- their rank/frequency distribution and the most highly used search terms. On average, there were 3. 36 image queries per user containing an average of 3.74 terms per query. Image queries contained a large number of unique terms. The most frequently occurring image related terms appeared less than 10 percent of the time, with most terms occurring only once. This analysis is contrasted to earlier work by Enser (1995) who examined written queries for pictorial information in a non-digital environment. Implications for the development of models for visual information retrieval, and for the design of Web search engines are discussed
    Series
    Proceedings of the American Society for Information Science; vol.36
    Source
    Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods
  12. Spink, A.; Ozmutlu, H.C.; Ozmutlu, S.: Multitasking information seeking and searching processes (2002) 0.01
    0.0051468783 = product of:
      0.020587513 = sum of:
        0.020587513 = weight(_text_:for in 600) [ClassicSimilarity], result of:
          0.020587513 = score(doc=600,freq=10.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.2319262 = fieldWeight in 600, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=600)
      0.25 = coord(1/4)
    
    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
  13. Spink, A.; Currier, J.: Towards an evolutionary perspective for human information behavior : an exploratory study (2006) 0.01
    0.0051468783 = product of:
      0.020587513 = sum of:
        0.020587513 = weight(_text_:for in 5592) [ClassicSimilarity], result of:
          0.020587513 = score(doc=5592,freq=10.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.2319262 = fieldWeight in 5592, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5592)
      0.25 = coord(1/4)
    
    Abstract
    Purpose - Since the beginning of human existence, humankind has sought, organized and used information as it evolved patterns and practices of human information behaviors. However, the field of human information behavior (HIB) has not heretofore pursued an evolutionary understanding of information behavior. The goal of this exploratory study is to provide insight about the information behavior of various individuals from the past to begin the development of an evolutionary perspective for our understanding of HIB. Design/methodology/approach - This paper presents findings from a qualitative analysis of the autobiographies and personal writings of several historical figures, including Napoleon Bonaparte, Charles Darwin, Giacomo Casanova and others. Findings - Analysis of their writings shows that these persons of the past articulated aspects of their HIB's, including information seeking, information organization and information use, providing tangible insights into their information-related thoughts and actions. Practical implications - This paper has implications for expanding the nature of our evolutionary understanding of information behavior and provides a broader context for the HIB research field. Originality/value - This the first paper in the information science field of HIB to study the information behavior of historical figures and begin to develop an evolutionary framework for HIB research.
  14. Jansen, B.J.; Spink, A.; Blakely, C.; Koshman, S.: Defining a session on Web search engines (2007) 0.01
    0.0051468783 = product of:
      0.020587513 = sum of:
        0.020587513 = weight(_text_:for in 285) [ClassicSimilarity], result of:
          0.020587513 = score(doc=285,freq=10.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.2319262 = fieldWeight in 285, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=285)
      0.25 = coord(1/4)
    
    Abstract
    Detecting query reformulations within a session by a Web searcher is an important area of research for designing more helpful searching systems and targeting content to particular users. Methods explored by other researchers include both qualitative (i.e., the use of human judges to manually analyze query patterns on usually small samples) and nondeterministic algorithms, typically using large amounts of training data to predict query modification during sessions. In this article, we explore three alternative methods for detection of session boundaries. All three methods are computationally straightforward and therefore easily implemented for detection of session changes. We examine 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005. We compare session analysis using (a) Internet Protocol address and cookie; (b) Internet Protocol address, cookie, and a temporal limit on intrasession interactions; and (c) Internet Protocol address, cookie, and query reformulation patterns. Overall, our analysis shows that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions. Regardless of the method used, the mean session length was fewer than three queries, and the mean session duration was less than 30 min. Searchers most often modified their query by changing query terms (nearly 23% of all query modifications) rather than adding or deleting terms. Implications are that for measuring searching traffic, unique sessions may be a better indicator than the common metric of unique visitors. This research also sheds light on the more complex aspects of Web searching involving query modifications and may lead to advances in searching tools.
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.862-871
  15. Spink, A.; Saracevic, T.: Interaction in information retrieval : selection and effectiveness of search terms (1997) 0.00
    0.004784106 = product of:
      0.019136423 = sum of:
        0.019136423 = weight(_text_:for in 206) [ClassicSimilarity], result of:
          0.019136423 = score(doc=206,freq=6.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.21557912 = fieldWeight in 206, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.046875 = fieldNorm(doc=206)
      0.25 = coord(1/4)
    
    Abstract
    We investigated the sources and effectiveness of search terms used during mediated on-line searching under real-life (as opposed to laboratory) circumstances. A stratified model of information retrieval (IR) interaction served as a framework for the analysis. For the analysis, we used the on-line transaction logs, videotapes, and transcribed dialogue of the presearch and on-line interaction between 40 users and 4 professional intermediaries. Each user provided one question and interacted with one of the four intermediaries. Searching was done using DIALOG. Five sources of search terms were identified: (1) the users' written question statements, (2) terms derived from users' domain knowledge during the interaction, (3) terms extracted from retrieved items as relevance feedback, (4) database thesaurus, and (5) terms derived by intermediaries during the interaction. Distribution, retrieval effectiveness, transition sequences, and correlation of search terms from different sources were investigated. Search terms from users' written question statements and term relevance feedback were the most productive sources of terms contributing to the retrieval of items judged relevant by users. Implications of the findings are discussed
    Source
    Journal of the American Society for Information Science. 48(1997) no.8, S.741-761
  16. Jansen, B.J.; Spink, A.; Pedersen, J.: ¬A temporal comparison of AItaVista Web searching (2005) 0.00
    0.004784106 = product of:
      0.019136423 = sum of:
        0.019136423 = weight(_text_:for in 3454) [ClassicSimilarity], result of:
          0.019136423 = score(doc=3454,freq=6.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.21557912 = fieldWeight in 3454, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.046875 = fieldNorm(doc=3454)
      0.25 = coord(1/4)
    
    Abstract
    Major Web search engines, such as AItaVista, are essential tools in the quest to locate online information. This article reports research that used transaction log analysis to examine the characteristics and changes in AItaVista Web searching that occurred from 1998 to 2002. The research questions we examined are (1) What are the changes in AItaVista Web searching from 1998 to 2002? (2) What are the current characteristics of AItaVista searching, including the duration and frequency of search sessions? (3) What changes in the information needs of AItaVista users occurred between 1998 and 2002? The results of our research show (1) a move toward more interactivity with increases in session and query length, (2) with 70% of session durations at 5 minutes or less, the frequency of interaction is increasing, but it is happening very quickly, and (3) a broadening range of Web searchers' information needs, with the most frequent terms accounting for less than 1% of total term usage. We discuss the implications of these findings for the development of Web search engines.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.6, S.559-570
  17. Desai, M.; Spink, A.: ¬A algorithm to cluster documents based on relevance (2005) 0.00
    0.004784106 = product of:
      0.019136423 = sum of:
        0.019136423 = weight(_text_:for in 1035) [ClassicSimilarity], result of:
          0.019136423 = score(doc=1035,freq=6.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.21557912 = fieldWeight in 1035, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.046875 = fieldNorm(doc=1035)
      0.25 = coord(1/4)
    
    Abstract
    Search engines fail to make a clear distinction between items of varying relevance when presenting search results to users. Instead, they rely on the user of the system to estimate which items are relevant, partially relevant, or not relevant. The user of the system is given the task of distinguishing between documents that are relevant to different degrees. This process often hinders the accessibility of relevant or partially relevant documents, particularly when the results set is large and documents of varying relevance are scattered throughout the set. In this paper, we present a clustering scheme that groups documents within relevant, partially relevant, and not relevant regions for a given search. A clustering algorithm accomplishes the task of clustering documents based on relevance. The clusters were evaluated by end-users issuing categorical, interval, and descriptive relevance judgments for the documents returned from a search. The degree of overlap between users and the system for each of the clustered regions was measured to determine the overall effectiveness of the algorithm. This research showed that clustering documents on the Web by regions of relevance is highly necessary and quite feasible.
  18. Spink, A.; Greisdorf, H.: Regions and levels : Measuring and mapping users' relevance judgements (2001) 0.00
    0.0046035075 = product of:
      0.01841403 = sum of:
        0.01841403 = weight(_text_:for in 5586) [ClassicSimilarity], result of:
          0.01841403 = score(doc=5586,freq=8.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.20744109 = fieldWeight in 5586, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5586)
      0.25 = coord(1/4)
    
    Abstract
    The dichotomous bipolar approach to relevance has produced an abundance of information retrieval (M) research. However, relevance studies that include consideration of users' partial relevance judgments are moving to a greater relevance clarity and congruity to impact the design of more effective [R systems. The study reported in this paper investigates the various regions of across a distribution of users' relevance judgments, including how these regions may be categorized, measured, and evaluated. An instrument was designed using four scales for collecting, measuring, and describing enduser relevance judgments. The instrument was administered to 21 end-users who conducted searches on their own information problems and made relevance judgments on a total of 1059 retrieved items. Findings include: (1) overlapping regions of relevance were found to impact the usefulness of precision ratios as a measure of IR system effectiveness, (2) both positive and negative levels of relevance are important to users as they make relevance judgments, (3) topicality was used more to reject rather than accept items as highly relevant, (4) utility was more used to judge items highly relevant, and (5) the nature of relevance judgment distribution suggested a new IR evaluation measure-median effect. Findings suggest that the middle region of a distribution of relevance judgments, also called "partial relevance," represents a key avenue for ongoing study. The findings provide implications for relevance theory, and the evaluation of IR systems
    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.2, S.161-173
  19. Spink, A.; Jansen, B.J.; Pedersen , J.: Searching for people on Web search engines (2004) 0.00
    0.0046035075 = product of:
      0.01841403 = sum of:
        0.01841403 = weight(_text_:for in 4429) [ClassicSimilarity], result of:
          0.01841403 = score(doc=4429,freq=8.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.20744109 = fieldWeight in 4429, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4429)
      0.25 = coord(1/4)
    
    Abstract
    The Web is a communication and information technology that is often used for the distribution and retrieval of personal information. Many people and organizations mount Web sites containing large amounts of information on individuals, particularly about celebrities. However, limited studies have examined how people search for information on other people, using personal names, via Web search engines. Explores the nature of personal name searching on Web search engines. The specific research questions addressed in the study are: "Do personal names form a major part of queries to Web search engines?"; "What are the characteristics of personal name Web searching?"; and "How effective is personal name Web searching?". Random samples of queries from two Web search engines were analyzed. The findings show that: personal name searching is a common but not a major part of Web searching with few people seeking information on celebrities via Web search engines; few personal name queries include double quotations or additional identifying terms; and name searches on Alta Vista included more advanced search features relative to those on AlltheWeb.com. Discusses the implications of the findings for Web searching and search engines, and further research.
  20. Jansen, B.J.; Spink, A.; Koshman, S.: Web searcher interaction with the Dogpile.com metasearch engine (2007) 0.00
    0.0046035075 = product of:
      0.01841403 = sum of:
        0.01841403 = weight(_text_:for in 270) [ClassicSimilarity], result of:
          0.01841403 = score(doc=270,freq=8.0), product of:
            0.08876751 = queryWeight, product of:
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.047278564 = queryNorm
            0.20744109 = fieldWeight in 270, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.8775425 = idf(docFreq=18385, maxDocs=44218)
              0.0390625 = fieldNorm(doc=270)
      0.25 = coord(1/4)
    
    Abstract
    Metasearch engines are an intuitive method for improving the performance of Web search by increasing coverage, returning large numbers of results with a focus on relevance, and presenting alternative views of information needs. However, the use of metasearch engines in an operational environment is not well understood. In this study, we investigate the usage of Dogpile.com, a major Web metasearch engine, with the aim of discovering how Web searchers interact with metasearch engines. We report results examining 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005 and compare these results with findings from other Web searching studies. We collect data on geographical location of searchers, use of system feedback, content selection, sessions, queries, and term usage. Findings show that Dogpile.com searchers are mainly from the USA (84% of searchers), use about 3 terms per query (mean = 2.85), implement system feedback moderately (8.4% of users), and generally (56% of users) spend less than one minute interacting with the Web search engine. Overall, metasearchers seem to have higher degrees of interaction than searchers on non-metasearch engines, but their sessions are for a shorter period of time. These aspects of metasearching may be what define the differences from other forms of Web searching. We discuss the implications of our findings in relation to metasearch for Web searchers, search engines, and content providers.
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.5, S.744-755