Search (74 results, page 2 of 4)

  • × author_ss:"Spink, A."
  1. Desai, M.; Spink, A.: ¬A algorithm to cluster documents based on relevance (2005) 0.00
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = product of:
          0.011481222 = sum of:
            0.011481222 = weight(_text_:a in 1035) [ClassicSimilarity], result of:
              0.011481222 = score(doc=1035,freq=16.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.2161963 = fieldWeight in 1035, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1035)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a
  2. Spink, A.; Gunar, O.: E-Commerce Web queries : Excite and AskJeeves study (2001) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 910) [ClassicSimilarity], result of:
              0.0108246 = score(doc=910,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 910, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=910)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
  3. Kuhlthau, C.; Spink, A.; Cool, C.: Exploration into stages in the retrieval in the information search process in online information retrieval : communication between users and intermediaries (1992) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 4518) [ClassicSimilarity], result of:
              0.0108246 = score(doc=4518,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 4518, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4518)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Describes a model of information seeking behaviour that views the information search process as proceeding through a series of cognitive states through which users progressively refine and reformulate their information problem. The model suggests that searches have several stages which evolve from vague and uncertain to clearer and directed and finally to focused and confident
    Type
    a
  4. Spink, A.: Multiple search sessions model of end-user behaviour : an exploratory study (1996) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 5805) [ClassicSimilarity], result of:
              0.0108246 = score(doc=5805,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 5805, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=5805)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Discusses a multiple search session model of end users' interaction with information retrieval systems based on results from an exploratory study investigating end users' search sessions over time with OPACs or CD-ROM databases at different stages of their information seeking related to a current research project. Interviews were conducted with 200 academic end users to investigate the occurrence of multiple search sessions
    Type
    a
  5. Khopkar, Y.; Spink, A.; Giles, C.L.; Shah, P.; Debnath, S.: Search engine personalization : An exploratory study (2003) 0.00
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = product of:
          0.0108246 = sum of:
            0.0108246 = weight(_text_:a in 384) [ClassicSimilarity], result of:
              0.0108246 = score(doc=384,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20383182 = fieldWeight in 384, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=384)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
  6. Wilson, T.D.; Ford, N.; Ellis, D.; Foster, A.; Spink, A.: Information seeking and mediated searching : Part 2: uncertainty and Its correlates (2002) 0.00
    0.0026849252 = product of:
      0.0053698504 = sum of:
        0.0053698504 = product of:
          0.010739701 = sum of:
            0.010739701 = weight(_text_:a in 5232) [ClassicSimilarity], result of:
              0.010739701 = score(doc=5232,freq=14.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20223314 = fieldWeight in 5232, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5232)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In "Part 2. Uncertainty and Its Correlates,'' where Wilson is the primary author, after a review of uncertainty as a concept in information seeking and decision research, it is hypothesized that if the Kuhlthau problem solving stage model is appropriate the searchers will recognize the stage in which they currently are operating. Secondly to test Wilson's contention that operationalized uncertainty would be useful in characterizing users, it is hypothesized that uncertainty will decrease as the searcher proceeds through problem stages and after the completion of the search. A review of pre and post search interviews reveals that uncertainty can be operationalized, and that academic researchers have no difficulty with a stage model of the information seeking process. Uncertainty is unrelated to sex, age, or discipline, but is related to problem stage and domain knowledge. Both concepts appear robust.
    Type
    a
  7. Jansen, B.J.; Booth, D.L.; Spink, A.: Determining the informational, navigational, and transactional intent of Web queries (2008) 0.00
    0.0026849252 = product of:
      0.0053698504 = sum of:
        0.0053698504 = product of:
          0.010739701 = sum of:
            0.010739701 = weight(_text_:a in 2091) [ClassicSimilarity], result of:
              0.010739701 = score(doc=2091,freq=14.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20223314 = fieldWeight in 2091, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2091)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a
  8. Spink, A.; Ozmutlu, H.C.; Ozmutlu, S.: Multitasking information seeking and searching processes (2002) 0.00
    0.0026742492 = product of:
      0.0053484985 = sum of:
        0.0053484985 = product of:
          0.010696997 = sum of:
            0.010696997 = weight(_text_:a in 600) [ClassicSimilarity], result of:
              0.010696997 = score(doc=600,freq=20.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20142901 = fieldWeight in 600, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  1.153047 = idf(docFreq=37942, 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.
    Type
    a
  9. Koshman, S.; Spink, A.; Jansen, B.J.: Web searching on the Vivisimo search engine (2006) 0.00
    0.0026742492 = product of:
      0.0053484985 = sum of:
        0.0053484985 = product of:
          0.010696997 = sum of:
            0.010696997 = weight(_text_:a in 216) [ClassicSimilarity], result of:
              0.010696997 = score(doc=216,freq=20.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20142901 = fieldWeight in 216, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=216)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The application of clustering to Web search engine technology is a novel approach that offers structure to the information deluge often faced by Web searchers. Clustering methods have been well studied in research labs; however, real user searching with clustering systems in operational Web environments is not well understood. This article reports on results from a transaction log analysis of Vivisimo.com, which is a Web meta-search engine that dynamically clusters users' search results. A transaction log analysis was conducted on 2-week's worth of data collected from March 28 to April 4 and April 25 to May 2, 2004, representing 100% of site traffic during these periods and 2,029,734 queries overall. The results show that the highest percentage of queries contained two terms. The highest percentage of search sessions contained one query and was less than 1 minute in duration. Almost half of user interactions with clusters consisted of displaying a cluster's result set, and a small percentage of interactions showed cluster tree expansion. Findings show that 11.1% of search sessions were multitasking searches, and there are a broad variety of search topics in multitasking search sessions. Other searching interactions and statistics on repeat users of the search engine are reported. These results provide insights into search characteristics with a cluster-based Web search engine and extend research into Web searching trends.
    Type
    a
  10. Jansen, B.J.; Spink, A.: ¬An analysis of Web searching by European Allthe Web.com users (2005) 0.00
    0.0026742492 = product of:
      0.0053484985 = sum of:
        0.0053484985 = product of:
          0.010696997 = sum of:
            0.010696997 = weight(_text_:a in 1015) [ClassicSimilarity], result of:
              0.010696997 = score(doc=1015,freq=20.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20142901 = fieldWeight in 1015, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1015)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The Web has become a worldwide source of information and a mainstream business tool. It is changing the way people conduct the daily business of their lives. As these changes are occurring, we need to understand what Web searching trends are emerging within the various global regions. What are the regional differences and trends in Web searching, if any? What is the effectiveness of Web search engines as providers of information? As part of a body of research studying these questions, we have analyzed two data sets collected from queries by mainly European users submitted to AlltheWeb.com on 6 February 2001 and 28 May 2002. AlltheWeb.com is a major and highly rated European search engine. Each data set contains approximately a million queries submitted by over 200,000 users and spans a 24-h period. This longitudinal benchmark study shows that European Web searching is evolving in certain directions. There was some decline in query length, with extremely simple queries. European search topics are broadening, with a notable percentage decline in sexual and pornographic searching. The majority of Web searchers view fewer than five Web documents, spending only seconds on a Web document. Approximately 50% of the Web documents viewed by these European users were topically relevant. We discuss the implications for Web information systems and information content providers.
    Type
    a
  11. Spink, A.; Greisdorf, H.: Regions and levels : Measuring and mapping users' relevance judgements (2001) 0.00
    0.0025370158 = product of:
      0.0050740317 = sum of:
        0.0050740317 = product of:
          0.010148063 = sum of:
            0.010148063 = weight(_text_:a in 5586) [ClassicSimilarity], result of:
              0.010148063 = score(doc=5586,freq=18.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19109234 = fieldWeight in 5586, product of:
                  4.2426405 = tf(freq=18.0), with freq of:
                    18.0 = termFreq=18.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5586)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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
    Type
    a
  12. Spink, A.; Park, M.: Information and non-information multitasking interplay (2005) 0.00
    0.0024857575 = product of:
      0.004971515 = sum of:
        0.004971515 = product of:
          0.00994303 = sum of:
            0.00994303 = weight(_text_:a in 4330) [ClassicSimilarity], result of:
              0.00994303 = score(doc=4330,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18723148 = fieldWeight in 4330, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4330)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Purpose - During multitasking, humans handle multiple tasks through task switching or engage in multitasking information behaviors. For example, a user switches between seeking new kitchen information and medical information. Recent studies provide insights these complex multitasking human information behaviors (HIB). However, limited studies have examined the interplay between information and non-information tasks. Design/methodology/approach - The goal of the paper was to examine the interplay of information and non-information task behaviors. Findings - This paper explores and speculates on a new direction in HIB research. The nature of HIB as a multitasking activity including the interplay of information and non-information behavior tasks, and the relation between multitasking information behavior to cognitive style and individual differences, is discussed. A model of multitasking between information and non-information behavior tasks is proposed. Practical implications/limitations - Multitasking information behavior models should include the interplay of information and non-information tasks, and individual differences and cognitive styles. Originality/value - The paper is the first information science theoretical examination of the interplay between information and non-information tasks.
    Type
    a
  13. Spink, A.; Ozmultu, H.C.: Characteristics of question format web queries : an exploratory study (2002) 0.00
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = product of:
          0.009567685 = sum of:
            0.009567685 = weight(_text_:a in 3910) [ClassicSimilarity], result of:
              0.009567685 = score(doc=3910,freq=16.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18016359 = fieldWeight in 3910, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3910)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Web queries in question format are becoming a common element of a user's interaction with Web search engines. Web search services such as Ask Jeeves - a publicly accessible question and answer (Q&A) search engine - request users to enter question format queries. This paper provides results from a study examining queries in question format submitted to two different Web search engines - Ask Jeeves that explicitly encourages queries in question format and the Excite search service that does not explicitly encourage queries in question format. We identify the characteristics of queries in question format in two different data sets: (1) 30,000 Ask Jeeves queries and 15,575 Excite queries, including the nature, length, and structure of queries in question format. Findings include: (1) 50% of Ask Jeeves queries and less than 1% of Excite were in question format, (2) most users entered only one query in question format with little query reformulation, (3) limited range of formats for queries in question format - mainly "where", "what", or "how" questions, (4) most common question query format was "Where can I find ..." for general information on a topic, and (5) non-question queries may be in request format. Overall, four types of user Web queries were identified: keyword, Boolean, question, and request. These findings provide an initial mapping of the structure and content of queries in question and request format. Implications for Web search services are discussed.
    Type
    a
  14. Jansen, B.J.; Spink, A.; Blakely, C.; Koshman, S.: Defining a session on Web search engines (2007) 0.00
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = product of:
          0.009567685 = sum of:
            0.009567685 = weight(_text_:a in 285) [ClassicSimilarity], result of:
              0.009567685 = score(doc=285,freq=16.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18016359 = fieldWeight in 285, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=285)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a
  15. Spink, A.; Goodrum, A.; Robins, D.: Search intermediary elicitations during mediated online searching (1995) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 3872) [ClassicSimilarity], result of:
              0.009471525 = score(doc=3872,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 3872, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=3872)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Investigates search intermediary elicitations during mediated online searching. A study of 40 online reference interviews involving 1.557 search intermediary elicitation, found 15 different types of search intermediary elicitation to users. The elicitation purpose included search terms and strategies, database selection, relevance of retrieved items, users' knowledge and previous information seeking. Analysis of the patterns in the types and sequencing of elicitation showed significant strings of multiple elicitation regarding search terms and strategies, and relevance judgements. Discusses the implications of the findings for training search intermediaries and the design of interfaces eliciting information from end users
    Type
    a
  16. Ozmutlu, S.; Spink, A.; Ozmutlu, H.C.: ¬A day in the life of Web searching : an exploratory study (2004) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 2530) [ClassicSimilarity], result of:
              0.009471525 = score(doc=2530,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 2530, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=2530)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Understanding Web searching behavior is important in developing more successful and cost-efficient Web search engines. We provide results from a comparative time-based Web study of US-based Excite and Norwegian-based Fast Web search logs, exploring variations in user searching related to changes in time of the day. Findings suggest: (1) fluctuations in Web user behavior over the day, (2) user investigations of query results are much longer, and submission of queries and number of users are much higher in the mornings, and (3) some query characteristics, including terms per query and query reformulation, remain steady throughout the day. Implications and further research are discussed.
    Type
    a
  17. Spink, A.; Saracevic, T.: Where do the search terms come from? (1992) 0.00
    0.0023435948 = product of:
      0.0046871896 = sum of:
        0.0046871896 = product of:
          0.009374379 = sum of:
            0.009374379 = weight(_text_:a in 4032) [ClassicSimilarity], result of:
              0.009374379 = score(doc=4032,freq=6.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17652355 = fieldWeight in 4032, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4032)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Presents selected results from a large study which observed under real-life conditions the interaction between users, intermediaries and computers before and during online searching. Concentrates on the sources of search terms and the relation between given search terms and retrieval of relevant and nonrelevant items as answers. Users provided the largest proportion of search terms (61%), followed by the thesuaurs (19%), relevance feedback (11%), and intermediary (9%). Only 4% of search terms resulted in retrieval of relevant items only; 60% retrieved relevant and nonrelevant items; 25% retrieved nonrelevant items only; and 11% retrieved nothing.
    Type
    a
  18. Spink, A.: Study of interactive feedback during mediated information retrieval (1997) 0.00
    0.0023435948 = product of:
      0.0046871896 = sum of:
        0.0046871896 = product of:
          0.009374379 = sum of:
            0.009374379 = weight(_text_:a in 158) [ClassicSimilarity], result of:
              0.009374379 = score(doc=158,freq=6.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17652355 = fieldWeight in 158, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0625 = fieldNorm(doc=158)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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
    Type
    a
  19. Spink, A.; Beatty, L.: Multiple search sessions by end-users of online catalogs and CD-ROM databases (1995) 0.00
    0.002269176 = product of:
      0.004538352 = sum of:
        0.004538352 = product of:
          0.009076704 = sum of:
            0.009076704 = weight(_text_:a in 3877) [ClassicSimilarity], result of:
              0.009076704 = score(doc=3877,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.1709182 = fieldWeight in 3877, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3877)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Reports from a study investigating the extent to which academic end users conduct multiple search sessions, over time woth OPAC or CD-ROM databases at different stages of their information seeking related to a current research project. Interviews were conducted using a questionnaire with 200 academic end users at Rutgers University Alexander Library, NJ and University of North Texas, to investigate the occurrence of multiple search sessions. Results show that at the time of the survey interview, 57% of end users had conducted multiple search sessions during their research project and 86% of end users conducted their 1st search session at the beginning stage of their information seeking process. 49% of end users had conducted between 1 and 6 search sessions and 8% more than 6 search sessions. 70% of multiple search sessionss end users had modified their search terms since their 1st search session. Discusses the implications of the findings for end user training, information retrieval systems design and further research
    Type
    a
  20. Jansen, B.J.; Spink, A.; Pedersen, J.: ¬A temporal comparison of AItaVista Web searching (2005) 0.00
    0.002269176 = product of:
      0.004538352 = sum of:
        0.004538352 = product of:
          0.009076704 = sum of:
            0.009076704 = weight(_text_:a in 3454) [ClassicSimilarity], result of:
              0.009076704 = score(doc=3454,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.1709182 = fieldWeight in 3454, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3454)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a

Types

  • a 70
  • el 3
  • m 2
  • More… Less…