Search (31 results, page 1 of 2)

  • × theme_ss:"Retrievalalgorithmen"
  1. 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
  2. Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983) 0.02
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    Date
    30. 3.2001 13:32:22
  3. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.02
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    Date
    25. 8.2005 17:42:22
  4. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:44
  5. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02
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    Date
    14. 6.2015 22:12:56
  6. Kaszkiel, M.; Zobel, J.: Effective ranking with arbitrary passages (2001) 0.02
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    Abstract
    Text retrieval systems store a great variety of documents, from abstracts, newspaper articles, and Web pages to journal articles, books, court transcripts, and legislation. Collections of diverse types of documents expose shortcomings in current approaches to ranking. Use of short fragments of documents, called passages, instead of whole documents can overcome these shortcomings: passage ranking provides convenient units of text to return to the user, can avoid the difficulties of comparing documents of different length, and enables identification of short blocks of relevant material among otherwise irrelevant text. In this article, we compare several kinds of passage in an extensive series of experiments. We introduce a new type of passage, overlapping fragments of either fixed or variable length. We show that ranking with these arbitrary passages gives substantial improvements in retrieval effectiveness over traditional document ranking schemes, particularly for queries on collections of long documents. Ranking with arbitrary passages shows consistent improvements compared to ranking with whole documents, and to ranking with previous passage types that depend on document structure or topic shifts in documents
  7. Zhu, J.; Han, L.; Gou, Z.; Yuan, X.: ¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems (2018) 0.01
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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.
  8. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01
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    Date
    20. 1.2007 18:30:22
  9. Faloutsos, C.: Signature files (1992) 0.01
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    Date
    7. 5.1999 15:22:48
  10. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01
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    Date
    22. 3.2003 19:27:23
  11. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01
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    Date
    22. 8.2014 17:05:18
  12. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01
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    Date
    13. 9.2014 14:45:22
  13. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  14. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.01
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    Date
    1. 8.1996 22:08:06
  15. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01
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    Date
    20. 3.2005 16:23:22
  16. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.01
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    Date
    12. 9.1996 13:56:22
  17. Kelledy, F.; Smeaton, A.F.: Signature files and beyond (1996) 0.01
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    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  18. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01
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    Date
    22. 3.2003 19:27:36
  19. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01
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    Date
    31. 5.2004 19:22:06
  20. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.01
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    Date
    11. 9.2004 17:32:22