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  • × theme_ss:"Retrievalalgorithmen"
  1. Savoy, J.: Ranking schemes in hybrid Boolean systems : a new approach (1997) 0.02 
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    Abstract
    In most commercial online systems, the retrieval system is based on the Boolean model and its inverted file organization. Since the investment in these systems is so great and changing them could be economically unfeasible, this article suggests a new ranking scheme especially adapted for hypertext environments in order to produce more effective retrieval results and yet maintain the effectiveness of the investment made to date in the Boolean model. To select the retrieved documents, the suggested ranking strategy uses multiple sources of document content evidence. The proposed scheme integrates both the information provided by the index and query terms, and the inherent relationships between documents such as bibliographic references or hypertext links. We will demonstrate that our scheme represents an integration of both subject and citation indexing, and results in a significant imporvement over classical ranking schemes uses in hybrid Boolean systems, while preserving its efficiency. Moreover, through knowing the nearest neighbor and the hypertext links which constitute additional sources of evidence, our strategy will take them into account in order to further improve retrieval effectiveness and to provide 'good' starting points for browsing in a hypertext or hypermedia environement
  2. 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
  3. Lopez-Pujalte, C.; Guerrero Bote, V.P.; Moya-Anegón, F. de: Evaluation of the application of genetic algorithms to relevance feedback (2003) 0.02 
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    Abstract
    We evaluated the different genetic algorithms applied to relevance feedback that are to be found in the literature and which follow the vector space model (the most commonly used model in this type of application). They were compared with a traditional relevance feedback algorithm - the Ide dec-hi method - since this had given the best results in the study of Salton & Buckley (1990) an this subject. The experiment was performed an the Cranfield collection, and the different algorithms were evaluated using the residual collection method (one of the most suitable methods for evaluating relevance feedback techniques). The results varied greatly depending an the fitness function that was used, from no improvement in some of the genetic algorithms, to a more than 127% improvement with one algorithm, surpassing even the traditional Ide dec-hi method. One can therefore conclude that genetic algorithms show great promise as an aid to implementing a truly effective information retrieval system.
  4. Hoenkamp, E.: Unitary operators on the document space (2003) 0.02 
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    Abstract
    When people search for documents, they eventually want content, not words. Hence, search engines should relate documents more by their underlying concepts than by the words they contain. One promising technique to do so is Latent Semantic Indexing (LSI). LSI dramatically reduces the dimension of the document space by mapping it into a space spanned by conceptual indices. Empirically, the number of concepts that can represent the documents are far fewer than the great variety of words in the textual representation. Although this almost obviates the problem of lexical matching, the mapping incurs a high computational cost compared to document parsing, indexing, query matching, and updating. This article accomplishes several things. First, it shows how the technique underlying LSI is just one example of a unitary operator, for which there are computationally more attractive alternatives. Second, it proposes the Haar transform as such an alternative, as it is memory efficient, and can be computed in linear to sublinear time. Third, it generalizes LSI by a multiresolution representation of the document space. The approach not only preserves the advantages of LSI at drastically reduced computational costs, it also opens a spectrum of possibilities for new research.
  5. Hubert, G.; Pitarch, Y.; Pinel-Sauvagnat, K.; Tournier, R.; Laporte, L.: TournaRank : when retrieval becomes document competition (2018) 0.02 
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    Abstract
    Numerous feature-based models have been recently proposed by the information retrieval community. The capability of features to express different relevance facets (query- or document-dependent) can explain such a success story. Such models are most of the time supervised, thus requiring a learning phase. To leverage the advantages of feature-based representations of documents, we propose TournaRank, an unsupervised approach inspired by real-life game and sport competition principles. Documents compete against each other in tournaments using features as evidences of relevance. Tournaments are modeled as a sequence of matches, which involve pairs of documents playing in turn their features. Once a tournament is ended, documents are ranked according to their number of won matches during the tournament. This principle is generic since it can be applied to any collection type. It also provides great flexibility since different alternatives can be considered by changing the tournament type, the match rules, the feature set, or the strategies adopted by documents during matches. TournaRank was experimented on several collections to evaluate our model in different contexts and to compare it with related approaches such as Learning To Rank and fusion ones: the TREC Robust2004 collection for homogeneous documents, the TREC Web2014 (ClueWeb12) collection for heterogeneous web documents, and the LETOR3.0 collection for comparison with supervised feature-based models.
  6. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  7. Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006) 0.01 
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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.
  8. 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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    Date
    30. 3.2001 13:32:22
  9. 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
  10. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:44
  11. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:56
  12. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.00 
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    Date
    20. 1.2007 18:30:22
  13. Faloutsos, C.: Signature files (1992) 0.00 
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    Date
    7. 5.1999 15:22:48
  14. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.00 
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    Date
    22. 3.2003 19:27:23
  15. Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.00 
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    Date
    22. 8.2014 17:05:18
  16. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.00 
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    Date
    13. 9.2014 14:45:22
  17. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.00 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
  18. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.00 
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    Date
    1. 8.1996 22:08:06
  19. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.00 
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    20. 3.2005 16:23:22
  20. Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996) 0.00 
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    Date
    12. 9.1996 13:56:22

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