Search (83 results, page 1 of 5)

0.14373547 = product of:
  0.2156032 = sum of:
    0.08901726 = weight(_text_:search in 1188) [ClassicSimilarity], result of:
      0.08901726 = score(doc=1188,freq=22.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.50944906 = fieldWeight in 1188, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.03125 = fieldNorm(doc=1188)
    0.12658595 = sum of:
      0.099340804 = weight(_text_:engines in 1188) [ClassicSimilarity], result of:
        0.099340804 = score(doc=1188,freq=6.0), product of:
          0.25542772 = queryWeight, product of:
            5.080822 = idf(docFreq=746, maxDocs=44218)
            0.05027291 = queryNorm
          0.38891944 = fieldWeight in 1188, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            5.080822 = idf(docFreq=746, maxDocs=44218)
            0.03125 = fieldNorm(doc=1188)
      0.027245143 = weight(_text_:22 in 1188) [ClassicSimilarity], result of:
        0.027245143 = score(doc=1188,freq=2.0), product of:
          0.17604718 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05027291 = queryNorm
          0.15476047 = fieldWeight in 1188, 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=1188)
  0.6666667 = coord(2/3)

Abstract

Surfing the World Wide Web used to be cool, dude, real cool. But things have gotten hot - so hot that finding something useful an the Web is no longer cool. It is suffocating Web searchers in the smoke and debris of mountain-sized lists of hits, decisions about which search engines they should use, whether they will get lost in the dizzying maze of a subject directory, use the right syntax for the search engine at hand, enter keywords that are likely to retrieve hits an the topics they have in mind, or enlist a browser that has sufficient functionality to display the most promising hits. When it comes to Web searching, in a few short years we have gone from the cool image of surfing the Web into the frying pan of searching the Web. We can turn down the heat by rethinking what Web searchers are doing and introduce some order into the chaos. Web search strategies that are tool-based-oriented to specific Web searching tools such as search en gines, subject directories, and meta search engines-have been widely promoted, and these strategies are just not working. It is time to dissect what Web searching tools expect from searchers and adjust our search strategies to these new tools. This discussion offers Web searchers help in the form of search strategies that are based an strategies that librarians have been using for a long time to search commercial information retrieval systems like Dialog, NEXIS, Wilsonline, FirstSearch, and Data-Star.

Content

"Web searching is different from searching commercial IR systems. We can learn from search strategies recommended for searching IR systems, but most won't be effective for Web searching. Web searchers need strate gies that let search engines do the job they were designed to do. This article presents six new Web searching strategies that do just that."

Date

22. 9.1997 19:16:05

0.12953952 = product of:
  0.19430926 = sum of:
    0.093939 = weight(_text_:search in 5009) [ClassicSimilarity], result of:
      0.093939 = score(doc=5009,freq=2.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.5376164 = fieldWeight in 5009, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.109375 = fieldNorm(doc=5009)
    0.10037026 = product of:
      0.20074052 = sum of:
        0.20074052 = weight(_text_:engines in 5009) [ClassicSimilarity], result of:
          0.20074052 = score(doc=5009,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.7858995 = fieldWeight in 5009, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.109375 = fieldNorm(doc=5009)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

0.11630317 = product of:
  0.17445475 = sum of:
    0.12426962 = weight(_text_:search in 199) [ClassicSimilarity], result of:
      0.12426962 = score(doc=199,freq=14.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.71119964 = fieldWeight in 199, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0546875 = fieldNorm(doc=199)
    0.05018513 = product of:
      0.10037026 = sum of:
        0.10037026 = weight(_text_:engines in 199) [ClassicSimilarity], result of:
          0.10037026 = score(doc=199,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39294976 = fieldWeight in 199, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0546875 = fieldNorm(doc=199)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Users' individual differences and tasks are important factors that influence the use of information systems. Two independent investigations were conducted to study the impact of differences in users' cognition and search tasks on Web search activities and outcomes. Strong task effects were found on search activities and outcomes, whereas interactions between cognitive and task variables were found on search activities only. These results imply that the flexibility of the Web and Web search engines allows different users to complete different search tasks successfully. However, the search techniques used and the efficiency of the searches appear to depend on how well the individual searcher fits with the specific task

0.11052248 = product of:
  0.16578372 = sum of:
    0.1299372 = weight(_text_:search in 0) [ClassicSimilarity], result of:
      0.1299372 = score(doc=0,freq=30.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.7436354 = fieldWeight in 0, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=0)
    0.03584652 = product of:
      0.07169304 = sum of:
        0.07169304 = weight(_text_:engines in 0) [ClassicSimilarity], result of:
          0.07169304 = score(doc=0,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.2806784 = fieldWeight in 0, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=0)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.

Content

Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks

0.10452528 = product of:
  0.15678792 = sum of:
    0.10609328 = weight(_text_:search in 198) [ClassicSimilarity], result of:
      0.10609328 = score(doc=198,freq=20.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.60717577 = fieldWeight in 198, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=198)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 198) [ClassicSimilarity], result of:
          0.10138928 = score(doc=198,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 198, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=198)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Conventional wisdom holds that queries to information retrieval systems will yield more relevant results if they contain multiple topic-related terms and use Boolean and phrase operators to enhance interpretation. Although studies have shown that the users of Web-based search engines typically enter short, term-based queries and rarely use search operators, little information exists concerning the effects of term and operator usage on the relevancy of search results. In this study, search engine users formulated queries on eight search topics. Each query was submitted to the user-specified search engine, and relevancy ratings for the retrieved pages were assigned. Expert-formulated queries were also submitted and provided a basis for comparing relevancy ratings across search engines. Data analysis based on our research model of the term and operator factors affecting relevancy was then conducted. The results show that the difference in the number of terms between expert and nonexpert searches, the percentage of matching terms between those searches, and the erroneous use of nonsupported operators in nonexpert searches explain most of the variation in the relevancy of search results. These findings highlight the need for designing search engine interfaces that provide greater support in the areas of term selection and operator usage

0.09705824 = product of:
  0.14558735 = sum of:
    0.09489272 = weight(_text_:search in 4904) [ClassicSimilarity], result of:
      0.09489272 = score(doc=4904,freq=16.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.54307455 = fieldWeight in 4904, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4904)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 4904) [ClassicSimilarity], result of:
          0.10138928 = score(doc=4904,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 4904, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4904)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Recent studies suggest that the wide variability in type, detail, and reliability of online information motivate expert searchers to develop procedural search knowledge. In contrast to prior research that has focused an finding relevant sources, procedural search knowledge focuses an how to order multiple relevant sources with the goal of retrieving comprehensive information. Because such procedural search knowledge is neither spontaneously inferred from the results of search engines, nor from the categories provided by domainspecific portals, the lack of such knowledge leads most novice searchers to retrieve incomplete information. In domains like healthcare, such incomplete information can lead to dangerous consequences. To address the above problem, a new kind of domain portal called a Strategy Hub was developed and tested. Strategy Hubs provide critical search procedures and associated high-quality links to enable users to find comprehensive and accurate information. We begin by describing how we collaborated with physicians to systematically identify generalizable search procedures to find comprehensive information about a disease, and how these search procedures were made available through the Strategy Hub. A controlled experiment suggests that this approach can improve the ability of novice searchers in finding comprehensive and accurate information, when compared to general-purpose search engines and domain-specific portals. We conclude with insights an how to refine and automate the Strategy Hub design, with the ultimate goal of helping users find more comprehensive information when searching in unfamiliar domains.

0.09442082 = product of:
  0.14163123 = sum of:
    0.0986154 = weight(_text_:search in 4483) [ClassicSimilarity], result of:
      0.0986154 = score(doc=4483,freq=12.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.5643796 = fieldWeight in 4483, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=4483)
    0.043015826 = product of:
      0.08603165 = sum of:
        0.08603165 = weight(_text_:engines in 4483) [ClassicSimilarity], result of:
          0.08603165 = score(doc=4483,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.33681408 = fieldWeight in 4483, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.046875 = fieldNorm(doc=4483)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper uses a mix of qualitative and quantitative methodology to analyse differences between Finnish and American web searchers (n=27 per country) in their choice of initial search strategies (direct address, subject directory and search engines) and their reasoning underlying these choices, with data gathered via a questionnaire. The paper looks at these differences for four types of questions with two variables: closed/open and predictable/unpredictable source of answer (n=16 questions per searcher; total n=864 questions). The paper found significant differences between the two groups' initial search strategies and for three of the four types of questions. The reasoning varied across countries and questions as well, with Finns mentioning fewer reasons although both groups mentioned in aggregate a total of 1,284 reasons in twenty-four reason categories. The reasoning indicated that both country groups considered not only question-related reasons but also source- and search-strategy related reasons in making their decision. The research raises questions about considering cultural differences in designing web search access mechanisms.

0.08858277 = product of:
  0.13287415 = sum of:
    0.08217951 = weight(_text_:search in 3684) [ClassicSimilarity], result of:
      0.08217951 = score(doc=3684,freq=12.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.47031635 = fieldWeight in 3684, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3684)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 3684) [ClassicSimilarity], result of:
          0.10138928 = score(doc=3684,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 3684, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3684)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The rapid development of Web sites providing extensive coverage of a topic, coupled with the development of powerful search engines (designed to help users find such Web sites), suggests that users can easily find comprehensive information about a topic. In domains such as consumer healthcare, finding comprehensive information about a topic is critical as it can improve a patient's judgment in making healthcare decisions, and can encourage higher compliance with treatment. However, recent studies show that despite using powerful search engines, many healthcare information seekers have difficulty finding comprehensive information even for narrow healthcare topics because the relevant information is scattered across many Web sites. To date, no studies have analyzed how facts related to a search topic are distributed across relevant Web pages and Web sites. In this study, the distribution of facts related to five common healthcare topics across high-quality sites is analyzed, and the reasons underlying those distributions are explored. The analysis revealed the existence of few pages that had many facts, many pages that had few facts, and no single page or site that provided all the facts. While such a distribution conforms to other information-related phenomena, a deeper analysis revealed that the distributions were caused by a trade-off between depth and breadth, leading to the existence of general, specialized, and sparse pages. Furthermore, the results helped to make explicit the knowledge needed by searchers to find comprehensive healthcare information, and suggested the motivation to explore distribution-conscious approaches for the development of future search systems, search interfaces, Web page designs, and training.

0.08858277 = product of:
  0.13287415 = sum of:
    0.08217951 = weight(_text_:search in 3910) [ClassicSimilarity], result of:
      0.08217951 = score(doc=3910,freq=12.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.47031635 = fieldWeight in 3910, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3910)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 3910) [ClassicSimilarity], result of:
          0.10138928 = score(doc=3910,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 3910, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3910)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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.

0.0871595 = product of:
  0.13073924 = sum of:
    0.09489272 = weight(_text_:search in 587) [ClassicSimilarity], result of:
      0.09489272 = score(doc=587,freq=16.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.54307455 = fieldWeight in 587, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=587)
    0.03584652 = product of:
      0.07169304 = sum of:
        0.07169304 = weight(_text_:engines in 587) [ClassicSimilarity], result of:
          0.07169304 = score(doc=587,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.2806784 = fieldWeight in 587, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=587)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.

0.07852928 = product of:
  0.11779392 = sum of:
    0.06709928 = weight(_text_:search in 5229) [ClassicSimilarity], result of:
      0.06709928 = score(doc=5229,freq=8.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.3840117 = fieldWeight in 5229, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5229)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 5229) [ClassicSimilarity], result of:
          0.10138928 = score(doc=5229,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 5229, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5229)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Thirty-one patrons, who were selected by Slone to provide a range of age and experience, agreed when approached while using the catalog of the Wake County library system to try searching via the Internet. Fifteen searched the Wake County online catalog in this manner and 16 searched the World Wide Web, including that catalog. They were subjected to brief pre-structured taped interviews before and after their searches and observed during the searching process resulting in a log of behaviors, comments, pages accessed, and time spent. Data were analyzed across participants and categories. Web searches were characterized as linking, URL, search engine, within a site domain, and searching a web catalog; and participants by the number of these techniques used. Four used only one, 13 used two, 11 used three, two used four, and one all five. Participant experience was characterized as never used, used search engines, browsing experience, email experience, URL experience, catalog experience, and finally chat room/newsgroup experience. Sixteen percent of the participants had never used the Internet, 71% had used search engines, 65% had browsed, 58% had used email, 39% had used URLs, 39% had used online catalogs, and 32% had used chat rooms. The catalog was normally consulted before the web, where both were used, and experience with an online catalog assists in web use. Scrolling was found to be unpopular and practiced halfheartedly.

0.07852928 = product of:
  0.11779392 = sum of:
    0.06709928 = weight(_text_:search in 2939) [ClassicSimilarity], result of:
      0.06709928 = score(doc=2939,freq=8.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.3840117 = fieldWeight in 2939, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2939)
    0.05069464 = product of:
      0.10138928 = sum of:
        0.10138928 = weight(_text_:engines in 2939) [ClassicSimilarity], result of:
          0.10138928 = score(doc=2939,freq=4.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.39693922 = fieldWeight in 2939, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2939)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Libraries, private and public, offer valuable resources to library patrons. As of today, the only way to locate information archived exclusively in libraries is through their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using specific keywords assigned to different fields of desired library catalog records, to obtain relevant results. These improperly formulated queries often yield irrelevant results or no results at all. This negative experience in dealing with existing library systems turns library patrons away from directly querying library catalogs; instead, they rely on Web search engines to perform their searches first, and upon obtaining the initial information (e.g., titles, subject headings, or authors) on the desired library materials, they query library catalogs. This searching strategy is an evidence of failure of today's library systems. In solving this problem, we propose an enhanced library system, which allows partial, similarity matching of (a) tags defined by ordinary users at a folksonomy site that describe the content of books and (b) unrestricted keywords specified by an ordinary library patron in a query to search for relevant library catalog records. The proposed library system allows patrons posting a query Q using commonly used words and ranks the retrieved results according to their degrees of resemblance with Q while maintaining the query processing time comparable with that achieved by current library search engines.

0.07516497 = product of:
  0.112747446 = sum of:
    0.06973162 = weight(_text_:search in 2441) [ClassicSimilarity], result of:
      0.06973162 = score(doc=2441,freq=6.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.39907667 = fieldWeight in 2441, 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=2441)
    0.043015826 = product of:
      0.08603165 = sum of:
        0.08603165 = weight(_text_:engines in 2441) [ClassicSimilarity], result of:
          0.08603165 = score(doc=2441,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.33681408 = fieldWeight in 2441, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.046875 = fieldNorm(doc=2441)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

It is widely believed that many queries submitted to search engines are inherently ambiguous (e.g., java and apple). However, few studies have tried to classify queries based on ambiguity and to answer "what the proportion of ambiguous queries is". This paper deals with these issues. First, we clarify the definition of ambiguous queries by constructing the taxonomy of queries from being ambiguous to specific. Second, we ask human annotators to manually classify queries. From manually labeled results, we observe that query ambiguity is to some extent predictable. Third, we propose a supervised learning approach to automatically identify ambiguous queries. Experimental results show that we can correctly identify 87% of labeled queries with the approach. Finally, by using our approach, we estimate that about 16% of queries in a real search log are ambiguous.

0.07461396 = product of:
  0.11192093 = sum of:
    0.09489272 = weight(_text_:search in 1073) [ClassicSimilarity], result of:
      0.09489272 = score(doc=1073,freq=16.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.54307455 = fieldWeight in 1073, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1073)
    0.017028214 = product of:
      0.03405643 = sum of:
        0.03405643 = weight(_text_:22 in 1073) [ClassicSimilarity], result of:
          0.03405643 = score(doc=1073,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.19345059 = fieldWeight in 1073, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1073)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The study analyses how students' growing understanding of the topic and search experience were related to their choice of search tactics and terms while preparing a research proposal for a small empirical study. In addition to that, the findings of the study are used to test Vakkari's (2001) theory of task-based IR. The research subjects were 22 students of psychology attending a seminar for preparing the proposal. They made a search for their task in PsychINFO database at the beginning and end of the seminar. Data were collected in several ways. A pre- and post-search interview was conducted in both sessions. The students were asked to think aloud in the sessions. This was recorded as were the transaction logs. The results show that search experience was slightly related to the change of facets. Although the students' vocabulary of the topic grew generating an increased use of specific terms between the sessions, their use of search tactics and operators remained fairly constant. There was no correlation between the terms and tactics used and the total number of useful references found. By comparing these results with the findings of relevant earlier studies the conclusion was drawn that domain knowledge has an impact on searching assuming that users have a sufficient command of the system used. This implies that the tested theory of task-based IR is valid on condition that the searchers are experienced. It is suggested that the theory should be enriched by including search experience in its scope.

0.067302 = product of:
  0.100953 = sum of:
    0.08051914 = weight(_text_:search in 3450) [ClassicSimilarity], result of:
      0.08051914 = score(doc=3450,freq=8.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.460814 = fieldWeight in 3450, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=3450)
    0.020433856 = product of:
      0.040867712 = sum of:
        0.040867712 = weight(_text_:22 in 3450) [ClassicSimilarity], result of:
          0.040867712 = score(doc=3450,freq=2.0), product of:
            0.17604718 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05027291 = queryNorm
            0.23214069 = fieldWeight in 3450, 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=3450)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Most information systems share a common assumption: information seeking is discrete. Such an assumption neither reflects real-life information seeking processes nor conforms to the perspective of phenomenology, "life is a journey constituted by continuous acquisition of knowledge." Thus, this study develops and validates a theoretical model that explains successive search experience for essentially the same information problem. The proposed model is called Multiple Information Seeking Episodes (MISE), which consists of four dimensions: problematic situation, information problem, information seeking process, episodes. Eight modes of multiple information seeking episodes are identified and specified with properties of the four dimensions of MISE. The results partially validate MISE by finding that the original MISE model is highly accurate, but less sufficient in characterizing successive searches; all factors in the MISE model are empirically confirmed, but new factors are identified as weIl. The revised MISE model is shifted from the user-centered to the interaction-centered perspective, taking into account factors of searcher, system, search activity, search context, information attainment, and information use activities.

Date

10. 4.2005 14:52:22

0.066634305 = product of:
  0.09995145 = sum of:
    0.056935627 = weight(_text_:search in 3269) [ClassicSimilarity], result of:
      0.056935627 = score(doc=3269,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.3258447 = fieldWeight in 3269, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=3269)
    0.043015826 = product of:
      0.08603165 = sum of:
        0.08603165 = weight(_text_:engines in 3269) [ClassicSimilarity], result of:
          0.08603165 = score(doc=3269,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.33681408 = fieldWeight in 3269, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.046875 = fieldNorm(doc=3269)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

Search engines are very popular tools for collecting information from distributed resources. They provide not only search facilities, but they also offer directories for users to browse content divided into groups. In this paper, we've adopted an individual differences approach to explore user's attitudes towards various interface features provided by existing Web directories. Among a variety of individual differences, cognitive style is a particularly important characteristic that influences the effectiveness of information seeking. Empirical results indicate that users' cognitive styles influence their reactions to the organization of subject categories, presentation of the results, and screen layout. We developed a set of design guidelines an the basis of these results, and propose a flexible interface that adopts these guidelines to accommodate the preferences of different cognitive style groups.

0.066634305 = product of:
  0.09995145 = sum of:
    0.056935627 = weight(_text_:search in 2091) [ClassicSimilarity], result of:
      0.056935627 = score(doc=2091,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.3258447 = fieldWeight in 2091, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.046875 = fieldNorm(doc=2091)
    0.043015826 = product of:
      0.08603165 = sum of:
        0.08603165 = weight(_text_:engines in 2091) [ClassicSimilarity], result of:
          0.08603165 = score(doc=2091,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.33681408 = fieldWeight in 2091, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.046875 = fieldNorm(doc=2091)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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.

0.05552859 = product of:
  0.08329288 = sum of:
    0.04744636 = weight(_text_:search in 1081) [ClassicSimilarity], result of:
      0.04744636 = score(doc=1081,freq=4.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.27153727 = fieldWeight in 1081, 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=1081)
    0.03584652 = product of:
      0.07169304 = sum of:
        0.07169304 = weight(_text_:engines in 1081) [ClassicSimilarity], result of:
          0.07169304 = score(doc=1081,freq=2.0), product of:
            0.25542772 = queryWeight, product of:
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.05027291 = queryNorm
            0.2806784 = fieldWeight in 1081, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.080822 = idf(docFreq=746, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1081)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

The aim of the work described in this paper is to evaluate the influencing effects of query-biased summaries in web searching. For this purpose, a summarisation system has been developed, and a summary tailored to the user's query is generated automatically for each document retrieved. The system aims to provide both a better means of assessing document relevance than titles or abstracts typical of many web search result lists. Through visiting each result page at retrieval-time, the system provides the user with an idea of the current page content and thus deals with the dynamic nature of the web. To examine the effectiveness of this approach, a task-oriented, comparative evaluation between four different web retrieval systems was performed; two that use query-biased summarisation, and two that use the standard ranked titles/abstracts approach. The results from the evaluation indicate that query-biased summarisation techniques appear to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach. The same methodology was used to compare the effectiveness of two of the web's major search engines; AltaVista and Google.

0.049510203 = product of:
  0.1485306 = sum of:
    0.1485306 = weight(_text_:search in 891) [ClassicSimilarity], result of:
      0.1485306 = score(doc=891,freq=20.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.8500461 = fieldWeight in 891, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0546875 = fieldNorm(doc=891)
  0.33333334 = coord(1/3)

Abstract

The study investigated how users' emotion control and search tasks interact and influence the Web search behavior and performance among experienced Web users. Sixty-seven undergraduate students with substantial Web experience participated in the study. Effects of emotion control and tasks were found significant on the search behavior but not on the search performance. The interaction effect between emotion control and tasks on the search behavior was also significant: effects of users' emotion control on the search behavior varied depending on search tasks. Profile analyses of search behaviors identified and contrasted the most commonly occurring profiles of search activities in different search tasks. Suggestions were made to improve information literacy programs, and implications for future research were discussed.

0.049510203 = product of:
  0.1485306 = sum of:
    0.1485306 = weight(_text_:search in 2093) [ClassicSimilarity], result of:
      0.1485306 = score(doc=2093,freq=20.0), product of:
        0.1747324 = queryWeight, product of:
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.05027291 = queryNorm
        0.8500461 = fieldWeight in 2093, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.475677 = idf(docFreq=3718, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2093)
  0.33333334 = coord(1/3)

Abstract

Transaction logs from four different Web-based information retrieval environments (bibliographic databank, OPAC, search engine, specialized search system) were analyzed for empirical regularities in search characteristics to determine whether users engage in different behaviors in different Web-based search environments. Descriptive statistics and relative frequency distributions related to term usage, query formulation, and session duration were tabulated. The analysis revealed that there are differences in these characteristics. Users were more likely to engage in extensive searching using the OPAC and specialized search system. Surprisingly, the bibliographic databank search environment resulted in the most parsimonious searching, more similar to a general search engine. Although on the surface Web-based search facilities may appear similar, users do engage in different search behaviors.