Search (109 results, page 1 of 6)

  • × theme_ss:"Retrievalalgorithmen"
  1. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.05
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    Date
    25. 8.2005 17:42:22
  2. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.05
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  3. Belkin, N.J.; Cool, C.; Koenemann, J.; Ng, K.B.; Park, S.: Using relevance feedback and ranking in interactive searching (1996) 0.04
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  4. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.04
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    Date
    1. 8.1996 22:08:06
  5. Faloutsos, C.: Signature files (1992) 0.03
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    Date
    7. 5.1999 15:22:48
  6. Lee, J.-T.; Seo, J.; Jeon, J.; Rim, H.-C.: Sentence-based relevance flow analysis for high accuracy retrieval (2011) 0.02
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  7. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.02
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    Abstract
    The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.
    Date
    22. 7.2006 16:32:43
  8. Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004) 0.02
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    Date
    16.11.2008 16:22:48
  9. Cheng, C.-S.; Chung, C.-P.; Shann, J.J.-J.: Fast query evaluation through document identifier assignment for inverted file-based information retrieval systems (2006) 0.02
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  10. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.02
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    Date
    11. 9.2004 17:32:22
  11. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 0.02
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  12. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.02
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  13. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.02
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    Abstract
    Recommender systems are effective in predicting the most suitable products for users, such as movies and books. To facilitate personalized recommendations, the quality of item ratings should be guaranteed. However, a few ratings might not be accurate enough due to the uncertainty of user behavior and are referred to as natural noise. In this article, we present a novel fuzzy clustering-based method for detecting noisy ratings. The entropy of a subset of the original ratings dataset is used to indicate the data-driven uncertainty, and evaluation metrics are adopted to represent the prediction-driven uncertainty. After the repetition of resampling and the execution of a recommendation algorithm, the entropy and evaluation metrics vectors are obtained and are empirically categorized to identify the proportion of the potential noise. Then, the fuzzy C-means-based denoising (FCMD) algorithm is performed to verify the natural noise under the assumption that natural noise is primarily the result of the exceptional behavior of users. Finally, a case study is performed using two real-world datasets. The experimental results show that our proposal outperforms previous proposals and has an advantage in dealing with natural noise.
  14. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  15. Salton, G.; Buckley, C.: Parallel text search methods (1988) 0.02
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  16. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.01
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  17. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01
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  18. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01
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  19. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.01
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  20. Cole, C.: Intelligent information retrieval: diagnosing information need : Part I: the theoretical framework for developing an intelligent IR tool (1998) 0.01
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Authors

Years

Languages

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  • m 1
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Types

  • a 102
  • m 3
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  • x 2
  • r 1
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