Search (17 results, page 1 of 1)

  • × theme_ss:"Suchmaschinen"
  • × theme_ss:"Retrievalalgorithmen"
  1. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.02 
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    Content
    Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading
    LCSH
    Text processing (Computer science)
    Subject
    Text processing (Computer science)
  2. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01 
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    Date
    25. 8.2005 17:42:22
  3. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.01 
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    Source
    Information processing and management. 50(2014) no.2, S.416-425
  4. 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
  5. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    Date
    20. 3.2005 16:23:22
  6. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.00 
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    Date
    11. 9.2004 17:32:22
  7. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.00 
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    LCSH
    Text processing (Computer science)
    Subject
    Text processing (Computer science)
  8. Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.63-69
  9. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.6, S.571-583
  10. Thelwall, M.; Vaughan, L.: New versions of PageRank employing alternative Web document models (2004) 0.00 
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    Abstract
    Introduces several new versions of PageRank (the link based Web page ranking algorithm), based on an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based on directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based on these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects' rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.
  11. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2229-2243
  12. Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006) 0.00 
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    Abstract
    Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and self-contained section for mathematics review.
  13. Meghabghab, G.: Google's Web page ranking applied to different topological Web graph structures (2001) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 52(2001) no.9, S.736-747
  14. White, R.W.; Jose, J.M.; Ruthven, I.: Using top-ranking sentences to facilitate effective information access (2005) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.10, S.1113-1125
  15. Watters, C.; Amoudi, A.: Geosearcher : location-based ranking of search engine results (2003) 0.00 
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    Source
    Journal of the American Society for Information Science and technology. 54(2003) no.2, S.140-151
  16. Chen, Z.; Meng, X.; Fowler, R.H.; Zhu, B.: Real-time adaptive feature and document learning for Web search (2001) 0.00 
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    Journal of the American Society for Information Science and technology. 52(2001) no.8, S.655-665
  17. Bilal, D.: Ranking, relevance judgment, and precision of information retrieval on children's queries : evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids (2012) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.9, S.1879-1896

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