Search (4 results, page 1 of 1)

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Abstract

Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

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Footnote

Das zweite Kapitel ("A Brief History of Wayfinding") beschreibt, wie Menschen sich in Umgebungen zurechtfinden. Dies ist insofern interessant, als hier nicht erst bei Informationssystemen oder dem WWW begonnen wird, sondern allgemeine Erkenntnisse beispielsweise über die Orientierung in natürlichen Umgebungen präsentiert werden. Viele typische Verhaltensweisen der Nutzer von Informationssystemen können so erklärt werden. So interessant dieses Thema allerdings ist, wirkt das Kapitel leider doch nur wie eine Zusammenstellung von Informationen aus zweiter Hand. Offensichtlich ist, dass Morville nicht selbst an diesen Themen geforscht hat, sondern die Ergebnisse (wenn auch auf ansprechende Weise) zusammengeschrieben hat. Dieser Eindruck bestätigt sich auch in weiteren Kapiteln: Ein flüssig geschriebener Text, der es jedoch an einigen Stellen an Substanz fehlen lässt. Kapitel drei, "Information Interaction" beginnt mit einem Rückgriff auf Calvin Mooers zentrale Aussage aus dem Jahre 1959: "An information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information than for him not to have it." In der Tat sollte man sich dies bei der Erstellung von Informationssystemen immer vergegenwärtigen; die Reihe der Systeme, die gerade an dieser Hürde gescheitert sind, ist lang. Das weitere Kapitel führt in einige zentrale Konzepte der Informationswissenschaft (Definition des Begriffs Information, Erläuterung des Information Retrieval, Wissensrepräsentation, Information Seeking Behaviour) ein, allerdings ohne jeden Anspruch auf Vollständigkeit. Es wirkt vielmehr so, dass der Autor sich die gerade für sein Anliegen passenden Konzepte auswählt und konkurrierende Ansätze beiseite lässt. Nur ein Beispiel: Im Abschnitt "Information Interaction" wird relativ ausführlich das Konzept des Berrypicking nach Marcia J. Bates präsentiert, allerdings wird es geradezu als exklusiv verkauft, was es natürlich bei weitem nicht ist. Natürlich kann es nicht Aufgabe dieses Buchs sein, einen vollständigen Überblick über alle Theorien des menschlichen Suchverhaltens zu geben (dies ist an anderer Stelle vorbildlich geleistet worden'), aber doch wenigstens der Hinweis auf einige zentrale Ansätze wäre angebracht gewesen. Spätestens in diesem Kapitel wird klar, dass das Buch sich definitiv nicht an Informationswissenschaftler wendet, die auf der einen Seite mit den grundlegenden Themen vertraut sein dürften, andererseits ein wenig mehr Tiefgang erwarten würden. Also stellt sich die Frage - und diese ist zentral für die Bewertung des gesamten Werks.

LCSH

Information storage and retrieval systems

RSWK

Information Retrieval (GBV)
Information Retrieval / Ubiquitous Computing (GBV)
Information Retrieval / Datenbanksystem / Suchmaschine (GBV)
Information Retrieval / Datenbanksystem (BVB)

Subject

Information Retrieval (GBV)
Information Retrieval / Ubiquitous Computing (GBV)
Information Retrieval / Datenbanksystem / Suchmaschine (GBV)
Information Retrieval / Datenbanksystem (BVB)
Information storage and retrieval systems

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Abstract

The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.

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)

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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