Search (170 results, page 1 of 9)

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  1. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.28 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  2. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.06 
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    Date
    25. 8.2005 17:42:22
  3. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.06 
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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
    RSWK
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
    Subject
    Suchmaschine / Information Retrieval
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
  4. Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006) 0.05 
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    Content
    Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling
    Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation
    RSWK
    Google / Suchmaschine / Ranking (BVB)
    Subject
    Google / Suchmaschine / Ranking (BVB)
  5. Chang, C.-H.; Hsu, C.-C.: Customizable multi-engine search tool with clustering (1997) 0.04 
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    Abstract
    Proposes a new idea of searching under the multi-engine search architecture to overcome the problems associated with relevance ranking. These include clustering of the search results and extraction of co-occurence keywords, which, with the user's feedback, better refines the query in the searching process. The system also provides the construction of the concept space to gradually customize the search tool to fit the usage for the user at the same time
    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 29(1997) no.8, S.1217-1224
  6. Baeza-Yates, R.; Boldi, P.; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms (2006) 0.04 
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    Abstract
    This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family. The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths. We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data. Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's tau on large domain datasets.
    Date
    16. 1.2016 10:22:28
  7. Carrière, S.J.; Kazman, R.: Webquery : searching and visualising the Web through connectivity (1997) 0.04 
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    Abstract
    The WebQuery system offers a powerful new method for searching the Web based on connectivity and content. Examines links among the nodes returned in a keyword-based query. Rankes the nodes, giving the highest rank to the most highly connected nodes. By doing so, finds hot spots on the Web that contain information germane to a user's query. WebQuery not only ranks and filters the results of a Web query; it also extends the result set beyond what the search engine retrieves, by finding interesting sites that are highly connected to those sites returned by the original query. Even with WebQuery filering and ranking query results, the result set can be enormous. Explores techniques for visualizing the returned information and discusses the criteria for using each of the technique
    Date
    1. 8.1996 22:08:06
    Source
    Computer networks and ISDN systems. 29(1997) no.8, S.1257-1267
  8. Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011) 0.04 
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    Abstract
    This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation.
    Date
    30. 9.2012 19:27:22
  9. Neibaur, A.R.: How to do everything with Yahoo! (2000) 0.04 
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    RSWK
    Internet / Suchmaschine
    Subject
    Internet / Suchmaschine
  10. Handbuch Internet-Suchmaschinen [1] : Nutzerorientierung in Wissenschaft und Praxis (2009) 0.04 
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    Content
    I. Suchmaschinenlandschaft Der Markt für Internet-Suchmaschinen - Christian Maaß, Andre Skusa, Andreas Heß und Gotthard Pietsch Typologie der Suchdienste im Internet - Joachim Griesbaum, Bernard Bekavac und Marc Rittberger Spezialsuchmaschinen - Dirk Lewandowski Suchmaschinenmarketing - Carsten D. Schultz II. Suchmaschinentechnologie Ranking-Verfahren für Web-Suchmaschinen - Philipp Dopichaj Programmierschnittstellen der kommerziellen Suchmaschinen - Fabio Tosques und Philipp Mayr Personalisierung der Internetsuche - Lösungstechniken und Marktüberblick - Kai Riemer und Fabian Brüggemann III. Nutzeraspekte Methoden der Erhebung von Nutzerdaten und ihre Anwendung in der Suchmaschinenforschung - Nadine Höchstötter Standards der Ergebnispräsentation - Dirk Lewandowski und Nadine Höchstötter Universal Search - Kontextuelle Einbindung von Ergebnissen unterschiedlicher Quellen und Auswirkungen auf das User Interface - Sonja Quirmbach Visualisierungen bei Internetsuchdiensten - Thomas Weinhold, Bernard Bekavac, Sonja Hierl, Sonja Öttl und Josef Herget IV. Recht und Ethik Datenschutz bei Suchmaschinen - Thilo Weichert Moral und Suchmaschinen - Karsten Weber V. Vertikale Suche Enterprise Search - Suchmaschinen für Inhalte im Unternehmen - Julian Bahrs Wissenschaftliche Dokumente in Suchmaschinen - Dirk Pieper und Sebastian Wolf Suchmaschinen für Kinder - Maria Zens, Friederike Silier und Otto Vollmers
    RSWK
    World Wide Web / Online-Recherche / Suchmaschine
    Subject
    World Wide Web / Online-Recherche / Suchmaschine
  11. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.03 
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    RSWK
    Suchmaschine / Information Retrieval
    World Wide Web / Suchmaschine / Mathematisches Modell (BVB)
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
    Subject
    Suchmaschine / Information Retrieval
    World Wide Web / Suchmaschine / Mathematisches Modell (BVB)
    Suchmaschine / Information Retrieval / Mathematisches Modell (HEBIS)
  12. Thelwall, M.: Directing students to new information types : a new role for Google in literature searches? (2005) 0.03 
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    Abstract
    Conducting a literature review is an important activity for postgraduates and many undergraduates. Librarians can play an important role, directing students to digital libraries, compiling online subject reSource lists, and educating about the need to evaluate the quality of online resources. In order to conduct an effective literature search in a new area, however, in some subjects it is necessary to gain basic topic knowledge, including specialist vocabularies. Google's link-based page ranking algorithm makes this search engine an ideal tool for finding specialist topic introductory material, particularly in computer science, and so librarians should be teaching this as part of a strategic literature review approach.
    Date
    3. 6.2007 16:37:29
  13. Hsieh-Yee, I.: ¬The retrieval power of selected search engines : how well do they address general reference questions and subject questions? (1998) 0.03 
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    Abstract
    Evaluates the performance of 8 major Internet search engines in answering 21 real reference questions and 5 made up subject questions. Reports on the retrieval and relevancy ranking abilities of the search engines. Concludes that the search engines did not produce good results for the reference questions unlike for the subject questions. The best engines are identified by type of questions, with Infoseek best for the subject questions, and OpenText best for refrence questions
    Date
    25.12.1998 19:22:51
  14. Lawrence, S.; Giles, C.L.: Inquirus, the NECI meta search engine (1998) 0.03 
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    Abstract
    Presents Inquirus, a WWW meta search engine which works by downloading and analysing the individual documents. It makes improvements over existing search engines in a number of areas: more useful document summaries incorporating query term context, identification of both pages which no longer exist and pages which no longer contain the query terms, advanced detection of duplicate pages, improved document ranking using proximity information, dramatically improved precision for certain queries by using specific expressive forms, and quick jump links and highlighting when viewing the full document
    Date
    1. 8.1996 22:08:06
  15. Zhitomirsky-Geffet, M.; Bar-Ilan, J.; Levene, M.: Analysis of change in users' assessment of search results over time (2017) 0.03 
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    Abstract
    We present the first systematic study of the influence of time on user judgements for rankings and relevance grades of web search engine results. The goal of this study is to evaluate the change in user assessment of search results and explore how users' judgements change. To this end, we conducted a large-scale user study with 86 participants who evaluated 2 different queries and 4 diverse result sets twice with an interval of 2 months. To analyze the results we investigate whether 2 types of patterns of user behavior from the theory of categorical thinking hold for the case of evaluation of search results: (a) coarseness and (b) locality. To quantify these patterns we devised 2 new measures of change in user judgements and distinguish between local (when users swap between close ranks and relevance values) and nonlocal changes. Two types of judgements were considered in this study: (a) relevance on a 4-point scale, and (b) ranking on a 10-point scale without ties. We found that users tend to change their judgements of the results over time in about 50% of cases for relevance and in 85% of cases for ranking. However, the majority of these changes were local.
    Date
    16.11.2017 13:33:29
  16. Pieper, D.; Wolf, S.: BASE - Eine Suchmaschine für OAI-Quellen und wissenschaftliche Webseiten (2007) 0.03 
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    Abstract
    Dieser Aufsatz beschreibt die Entwicklung der Suchmaschine BASE (Bielefeld Academic Search Engine) seit 2005. In dieser Zeit wurde der Index um ein Vielfaches ausgebaut und auch die Nutzungszahlen stiegen deutlich. Der Schwerpunkt liegt auf der Indexierung von Dokumentenservern, die ihre Daten über das "Protocol for Metadata Harvesting" (OAI-PMH) bereitstellen. Im Gegensatz zu speziellen OAI-Suchmaschine wie OAIster verfügt BASE jedoch über weitergehende Suchmöglichkeiten und indexiert auch wissenschaftliche Webseiten mit Hilfe eines integrierten Web-Crawlers und andere Quellen, wie zum Beispiel den Bibliothekskatalog der Universitätsbibliothek Bielefeld. BASE kommt darüber hinaus als Suchsystem auch in anderen Bereichen, zum Beispiel im Bielefeld eScholarship Repository, als Suchmaschine der Universität Bielefeld und im EU-Projekt DRIVER via Schnittstellen zum BASE-Index zum Einsatz.
  17. Meghabghab, G.: Google's Web page ranking applied to different topological Web graph structures (2001) 0.03 
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    Abstract
    This research is part of the ongoing study to better understand web page ranking on the web. It looks at a web page as a graph structure or a web graph, and tries to classify different web graphs in the new coordinate space: (out-degree, in-degree). The out-degree coordinate od is defined as the number of outgoing web pages from a given web page. The in-degree id coordinate is the number of web pages that point to a given web page. In this new coordinate space a metric is built to classify how close or far different web graphs are. Google's web ranking algorithm (Brin & Page, 1998) on ranking web pages is applied in this new coordinate space. The results of the algorithm has been modified to fit different topological web graph structures. Also the algorithm was not successful in the case of general web graphs and new ranking web algorithms have to be considered. This study does not look at enhancing web ranking by adding any contextual information. It only considers web links as a source to web page ranking. The author believes that understanding the underlying web page as a graph will help design better ranking web algorithms, enhance retrieval and web performance, and recommends using graphs as a part of visual aid for browsing engine designers
  18. Page, L.; Brin, S.; Motwani, R.; Winograd, T.: ¬The PageRank citation ranking : Bringing order to the Web (1999) 0.03 
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  19. Laursen, J.V.: Somebody wants to get in touch with you : search engine persuation (1998) 0.03 
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    Abstract
    Looks at some of the ways Web pages are designed to improve their ranking by Internet search engines. This is known as search engine persuation (SEP) and it has added a new obstacle to eliminating junk and obtaining successful search results. SEP methods include: taking advantage of ranking principles, abuse of the meta tag which describes the page, misusing the meta tag on the Netscape browser which indicates numbers of other pages linked to that page, and the growth of companies offering to improve ranking
  20. Park, E.-K.; Ra, D.-Y.; Jang, M.-G.: Techniques for improving web retrieval effectiveness (2005) 0.03 
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    Abstract
    This paper talks about several schemes for improving retrieval effectiveness that can be used in the named page finding tasks of web information retrieval (Overview of the TREC-2002 web track. In: Proceedings of the Eleventh Text Retrieval Conference TREC-2002, NIST Special Publication #500-251, 2003). These methods were applied on top of the basic information retrieval model as additional mechanisms to upgrade the system. Use of the title of web pages was found to be effective. It was confirmed that anchor texts of incoming links was beneficial as suggested in other works. Sentence-query similarity is a new type of information proposed by us and was identified to be the best information to take advantage of. Stratifying and re-ranking the retrieval list based on the maximum count of index terms in common between a sentence and a query resulted in significant improvement of performance. To demonstrate these facts a large-scale web information retrieval system was developed and used for experimentation.
    Date
    26.12.2007 20:28:29

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