Search (14 results, page 1 of 1)

  • × author_ss:"Järvelin, K."
  1. Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017) 0.07
    0.073638 = product of:
      0.110456996 = sum of:
        0.09002314 = weight(_text_:search in 3589) [ClassicSimilarity], result of:
          0.09002314 = score(doc=3589,freq=10.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.51520574 = fieldWeight in 3589, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.046875 = fieldNorm(doc=3589)
        0.020433856 = product of:
          0.040867712 = sum of:
            0.040867712 = weight(_text_:22 in 3589) [ClassicSimilarity], result of:
              0.040867712 = score(doc=3589,freq=2.0), product of:
                0.17604718 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05027291 = queryNorm
                0.23214069 = fieldWeight in 3589, 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=3589)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered.
  2. Ferro, N.; Silvello, G.; Keskustalo, H.; Pirkola, A.; Järvelin, K.: ¬The twist measure for IR evaluation : taking user's effort into account (2016) 0.05
    0.04626411 = product of:
      0.06939616 = sum of:
        0.03354964 = weight(_text_:search in 2771) [ClassicSimilarity], result of:
          0.03354964 = score(doc=2771,freq=2.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.19200584 = fieldWeight in 2771, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2771)
        0.03584652 = product of:
          0.07169304 = sum of:
            0.07169304 = weight(_text_:engines in 2771) [ClassicSimilarity], result of:
              0.07169304 = score(doc=2771,freq=2.0), product of:
                0.25542772 = queryWeight, product of:
                  5.080822 = idf(docFreq=746, maxDocs=44218)
                  0.05027291 = queryNorm
                0.2806784 = fieldWeight in 2771, 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=2771)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    We present a novel measure for ranking evaluation, called Twist (t). It is a measure for informational intents, which handles both binary and graded relevance. t stems from the observation that searching is currently a that searching is currently taken for granted and it is natural for users to assume that search engines are available and work well. As a consequence, users may assume the utility they have in finding relevant documents, which is the focus of traditional measures, as granted. On the contrary, they may feel uneasy when the system returns nonrelevant documents because they are then forced to do additional work to get the desired information, and this causes avoidable effort. The latter is the focus of t, which evaluates the effectiveness of a system from the point of view of the effort required to the users to retrieve the desired information. We provide a formal definition of t, a demonstration of its properties, and introduce the notion of effort/gain plots, which complement traditional utility-based measures. By means of an extensive experimental evaluation, t is shown to grasp different aspects of system performances, to not require extensive and costly assessments, and to be a robust tool for detecting differences between systems.
  3. Pirkola, A.; Järvelin, K.: Employing the resolution power of search keys (2001) 0.03
    0.031313002 = product of:
      0.093939 = sum of:
        0.093939 = weight(_text_:search in 5907) [ClassicSimilarity], result of:
          0.093939 = score(doc=5907,freq=8.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.5376164 = fieldWeight in 5907, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5907)
      0.33333334 = coord(1/3)
    
    Abstract
    Search key resolution power is analyzed in the context of a request, i.e., among the set of search keys for the request. Methods of characterizing the resolution power of keys automatically are studied, and the effects search keys of varying resolution power have on retrieval effectiveness are analyzed. It is shown that it often is possible to identify the best key of a query while the discrimination between the remaining keys presents problems. It is also shown that query performance is improved by suitably using the best key in a structured query. The tests were run with InQuery in a subcollection of the TREC collection, which contained some 515,000 documents
  4. Pharo, N.; Järvelin, K.: ¬The SST method : a tool for analysing Web information search processes (2004) 0.03
    0.029588005 = product of:
      0.08876401 = sum of:
        0.08876401 = weight(_text_:search in 2533) [ClassicSimilarity], result of:
          0.08876401 = score(doc=2533,freq=14.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.5079997 = fieldWeight in 2533, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2533)
      0.33333334 = coord(1/3)
    
    Abstract
    The article presents the search situation transition (SST) method for analysing Web information search (WIS) processes. The idea of the method is to analyse searching behaviour, the process, in detail and connect both the searchers' actions (captured in a log) and his/her intentions and goals, which log analysis never captures. On the other hand, ex post factor surveys, while popular in WIS research, cannot capture the actual search processes. The method is presented through three facets: its domain, its procedure, and its justification. The method's domain is presented in the form of a conceptual framework which maps five central categories that influence WIS processes; the searcher, the social/organisational environment, the work task, the search task, and the process itself. The method's procedure includes various techniques for data collection and analysis. The article presents examples from real WIS processes and shows how the method can be used to identify the interplay of the categories during the processes. It is shown that the method presents a new approach in information seeking and retrieval by focusing on the search process as a phenomenon and by explicating how different information seeking factors directly affect the search process.
  5. Pharo, N.; Järvelin, K.: "Irrational" searchers and IR-rational researchers (2006) 0.02
    0.023243874 = product of:
      0.06973162 = sum of:
        0.06973162 = weight(_text_:search in 4922) [ClassicSimilarity], result of:
          0.06973162 = score(doc=4922,freq=6.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.39907667 = fieldWeight in 4922, 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=4922)
      0.33333334 = coord(1/3)
    
    Abstract
    In this article the authors look at the prescriptions advocated by Web search textbooks in the light of a selection of empirical data of real Web information search processes. They use the strategy of disjointed incrementalism, which is a theoretical foundation from decision making, to focus an how people face complex problems, and claim that such problem solving can be compared to the tasks searchers perform when interacting with the Web. The findings suggest that textbooks an Web searching should take into account that searchers only tend to take a certain number of sources into consideration, that the searchers adjust their goals and objectives during searching, and that searchers reconsider the usefulness of sources at different stages of their work tasks as well as their search tasks.
  6. Halttunen, K.; Järvelin, K.: Assessing learning outcomes in two information retrieval learning environments (2005) 0.02
    0.018978544 = product of:
      0.056935627 = sum of:
        0.056935627 = weight(_text_:search in 996) [ClassicSimilarity], result of:
          0.056935627 = score(doc=996,freq=4.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.3258447 = fieldWeight in 996, 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=996)
      0.33333334 = coord(1/3)
    
    Abstract
    In order to design information retrieval (IR) learning environments and instruction, it is important to explore learning outcomes of different pedagogical solutions. Learning outcomes have seldom been evaluated in IR instruction. The particular focus of this study is the assessment of learning outcomes in an experimental, but naturalistic, learning environment compared to more traditional instruction. The 57 participants of an introductory course on IR were selected for this study, and the analysis illustrates their learning outcomes regarding both conceptual change and development of IR skill. Concept mapping of student essays was used to analyze conceptual change and log-files of search exercises provided data for performance assessment. Students in the experimental learning environment changed their conceptions more regarding linguistic aspects of IR and paid more emphasis on planning and management of search process. Performance assessment indicates that anchored instruction and scaffolding with an instructional tool, the IR Game, with performance feedback enables students to construct queries with fewer semantic knowledge errors also in operational IR systems.
  7. Lehtokangas, R.; Keskustalo, H.; Järvelin, K.: Experiments with transitive dictionary translation and pseudo-relevance feedback using graded relevance assessments (2008) 0.01
    0.013419857 = product of:
      0.04025957 = sum of:
        0.04025957 = weight(_text_:search in 1349) [ClassicSimilarity], result of:
          0.04025957 = score(doc=1349,freq=2.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.230407 = fieldWeight in 1349, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.046875 = fieldNorm(doc=1349)
      0.33333334 = coord(1/3)
    
    Abstract
    In this article, the authors present evaluation results for transitive dictionary-based cross-language information retrieval (CLIR) using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. Source language topics (in English, German, and Swedish) were automatically translated into the target language (Finnish) via an intermediate (or pivot) language. Effectiveness of the transitively translated queries was compared to that of the directly translated and monolingual Finnish queries. Pseudo-relevance feedback (PRF) was also used to expand the original transitive target queries. Cross-language information retrieval performance was evaluated on three relevance thresholds: stringent, regular, and liberal. The transitive translations performed well achieving, on the average, 85-93% of the direct translation performance, and 66-72% of monolingual performance. Moreover, PRF was successful in raising the performance of transitive translation routes in absolute terms as well as in relation to monolingual and direct translation performance applying PRF.
  8. Sormunen, E.; Kekäläinen, J.; Koivisto, J.; Järvelin, K.: Document text characteristics affect the ranking of the most relevant documents by expanded structured queries (2001) 0.01
    0.011183213 = product of:
      0.03354964 = sum of:
        0.03354964 = weight(_text_:search in 4487) [ClassicSimilarity], result of:
          0.03354964 = score(doc=4487,freq=2.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.19200584 = fieldWeight in 4487, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4487)
      0.33333334 = coord(1/3)
    
    Abstract
    The increasing flood of documentary information through the Internet and other information sources challenges the developers of information retrieval systems. It is not enough that an IR system is able to make a distinction between relevant and non-relevant documents. The reduction of information overload requires that IR systems provide the capability of screening the most valuable documents out of the mass of potentially or marginally relevant documents. This paper introduces a new concept-based method to analyse the text characteristics of documents at varying relevance levels. The results of the document analysis were applied in an experiment on query expansion (QE) in a probabilistic IR system. Statistical differences in textual characteristics of highly relevant and less relevant documents were investigated by applying a facet analysis technique. In highly relevant documents a larger number of aspects of the request were discussed, searchable expressions for the aspects were distributed over a larger set of text paragraphs, and a larger set of unique expressions were used per aspect than in marginally relevant documents. A query expansion experiment verified that the findings of the text analysis can be exploited in formulating more effective queries for best match retrieval in the search for highly relevant documents. The results revealed that expanded queries with concept-based structures performed better than unexpanded queries or Ñnatural languageÒ queries. Further, it was shown that highly relevant documents benefit essentially more from the concept-based QE in ranking than marginally relevant documents.
  9. Kekäläinen, J.; Järvelin, K.: Using graded relevance assessments in IR evaluation (2002) 0.01
    0.011183213 = product of:
      0.03354964 = sum of:
        0.03354964 = weight(_text_:search in 5225) [ClassicSimilarity], result of:
          0.03354964 = score(doc=5225,freq=2.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.19200584 = fieldWeight in 5225, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5225)
      0.33333334 = coord(1/3)
    
    Abstract
    Kekalainen and Jarvelin use what they term generalized, nonbinary recall and precision measures where recall is the sum of the relevance scores of the retrieved documents divided by the sum of relevance scores of all documents in the data base, and precision is the sum of the relevance scores of the retrieved documents divided by the number of documents where the relevance scores are real numbers between zero and one. Using the In-Query system and a text data base of 53,893 newspaper articles with 30 queries selected from those for which four relevance categories to provide recall measures were available, search results were evaluated by four judges. Searches were done by average key term weight, Boolean expression, and by average term weight where the terms are grouped by a synonym operator, and for each case with and without expansion of the original terms. Use of higher standards of relevance appears to increase the superiority of the best method. Some methods do a better job of getting the highly relevant documents but do not increase retrieval of marginal ones. There is evidence that generalized precision provides more equitable results, while binary precision provides undeserved merit to some methods. Generally graded relevance measures seem to provide additional insight into IR evaluation.
  10. Saarikoski, J.; Laurikkala, J.; Järvelin, K.; Juhola, M.: ¬A study of the use of self-organising maps in information retrieval (2009) 0.01
    0.011183213 = product of:
      0.03354964 = sum of:
        0.03354964 = weight(_text_:search in 2836) [ClassicSimilarity], result of:
          0.03354964 = score(doc=2836,freq=2.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.19200584 = fieldWeight in 2836, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2836)
      0.33333334 = coord(1/3)
    
    Abstract
    Purpose - The aim of this paper is to explore the possibility of retrieving information with Kohonen self-organising maps, which are known to be effective to group objects according to their similarity or dissimilarity. Design/methodology/approach - After conventional preprocessing, such as transforming into vector space, documents from a German document collection were trained for a neural network of Kohonen self-organising map type. Such an unsupervised network forms a document map from which relevant objects can be found according to queries. Findings - Self-organising maps ordered documents to groups from which it was possible to find relevant targets. Research limitations/implications - The number of documents used was moderate due to the limited number of documents associated to test topics. The training of self-organising maps entails rather long running times, which is their practical limitation. In future, the aim will be to build larger networks by compressing document matrices, and to develop document searching in them. Practical implications - With self-organising maps the distribution of documents can be visualised and relevant documents found in document collections of limited size. Originality/value - The paper reports on an approach that can be especially used to group documents and also for information search. So far self-organising maps have rarely been studied for information retrieval. Instead, they have been applied to document grouping tasks.
  11. Ingwersen, P.; Järvelin, K.: ¬The turn : integration of information seeking and retrieval in context (2005) 0.01
    0.007907727 = product of:
      0.02372318 = sum of:
        0.02372318 = weight(_text_:search in 1323) [ClassicSimilarity], result of:
          0.02372318 = score(doc=1323,freq=4.0), product of:
            0.1747324 = queryWeight, product of:
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.05027291 = queryNorm
            0.13576864 = fieldWeight in 1323, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.475677 = idf(docFreq=3718, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1323)
      0.33333334 = coord(1/3)
    
    Abstract
    The Turn analyzes the research of information seeking and retrieval (IS&R) and proposes a new direction of integrating research in these two areas: the fields should turn off their separate and narrow paths and construct a new avenue of research. An essential direction for this avenue is context as given in the subtitle Integration of Information Seeking and Retrieval in Context. Other essential themes in the book include: IS&R research models, frameworks and theories; search and works tasks and situations in context; interaction between humans and machines; information acquisition, relevance and information use; research design and methodology based on a structured set of explicit variables - all set into the holistic cognitive approach. The present monograph invites the reader into a construction project - there is much research to do for a contextual understanding of IS&R. The Turn represents a wide-ranging perspective of IS&R by providing a novel unique research framework, covering both individual and social aspects of information behavior, including the generation, searching, retrieval and use of information. Regarding traditional laboratory information retrieval research, the monograph proposes the extension of research toward actors, search and work tasks, IR interaction and utility of information. Regarding traditional information seeking research, it proposes the extension toward information access technology and work task contexts. The Turn is the first synthesis of research in the broad area of IS&R ranging from systems oriented laboratory IR research to social science oriented information seeking studies. TOC:Introduction.- The Cognitive Framework for Information.- The Development of Information Seeking Research.- Systems-Oriented Information Retrieval.- Cognitive and User-Oriented Information Retrieval.- The Integrated IS&R Research Framework.- Implications of the Cognitive Framework for IS&R.- Towards a Research Program.- Conclusion.- Definitions.- References.- Index.
  12. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.01
    0.0068112854 = product of:
      0.020433856 = sum of:
        0.020433856 = product of:
          0.040867712 = sum of:
            0.040867712 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
              0.040867712 = score(doc=2230,freq=2.0), product of:
                0.17604718 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05027291 = queryNorm
                0.23214069 = fieldWeight in 2230, 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=2230)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Source
    Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
  13. Näppilä, T.; Järvelin, K.; Niemi, T.: ¬A tool for data cube construction from structurally heterogeneous XML documents (2008) 0.01
    0.0056760716 = product of:
      0.017028214 = sum of:
        0.017028214 = product of:
          0.03405643 = sum of:
            0.03405643 = weight(_text_:22 in 1369) [ClassicSimilarity], result of:
              0.03405643 = score(doc=1369,freq=2.0), product of:
                0.17604718 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05027291 = queryNorm
                0.19345059 = fieldWeight in 1369, 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=1369)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    9. 2.2008 17:22:42
  14. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.01
    0.0056760716 = product of:
      0.017028214 = sum of:
        0.017028214 = product of:
          0.03405643 = sum of:
            0.03405643 = weight(_text_:22 in 998) [ClassicSimilarity], result of:
              0.03405643 = score(doc=998,freq=2.0), product of:
                0.17604718 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.05027291 = queryNorm
                0.19345059 = fieldWeight in 998, 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=998)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    22. 6.2023 18:15:06