Search (191 results, page 1 of 10)

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Abstract

Chen et alia report on the design of FEATURES, a web search engine with adaptive features based on minimal relevance feedback. Rather than developing user profiles from previous searcher activity either at the server or client location, or updating indexes after search completion, FEATURES allows for index and user characterization files to be updated during query modification on retrieval from a general purpose search engine. Indexing terms relevant to a query are defined as the union of all terms assigned to documents retrieved by the initial search run and are used to build a vector space model on this retrieved set. The top ten weighted terms are presented to the user for a relevant non-relevant choice which is used to modify the term weights. Documents are chosen if their summed term weights are greater than some threshold. A user evaluation of the top ten ranked documents as non-relevant will decrease these term weights and a positive judgement will increase them. A new ordering of the retrieved set will generate new display lists of terms and documents. Precision is improved in a test on Alta Vista searches.

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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

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Abstract

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

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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

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Abstract

Caching of query results is an important mechanism for efficiency and scalability of web search engines. Query results are cached and presented in terms of pages, which typically include 10 results each. In navigational queries, users seek a particular website, which would be typically listed at the top ranks (maybe, first or second) by the search engine, if found. For this type of query, caching and presenting results in the 10-per-page manner may waste cache space and network bandwidth. In this article, we propose nonuniform result page models with varying numbers of results for navigational queries. The experimental results show that our approach reduces the cache miss count by up to 9.17% (because of better utilization of cache space). Furthermore, bandwidth usage, which is measured in terms of number of snippets sent, is also reduced by 71% for navigational queries. This means a considerable reduction in the number of transmitted network packets, i.e., a crucial gain especially for mobile-search scenarios. A user study reveals that users easily adapt to the proposed result page model and that the efficiency gains observed in the experiments can be carried over to real-life situations.

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Abstract

Purpose Web search is more and more moving into mobile contexts. However, screen size of mobile devices is limited and search engine result pages face a trade-off between offering informative snippets and optimal use of space. One factor clearly influencing this trade-off is snippet length. The purpose of this paper is to find out what snippet size to use in mobile web search. Design/methodology/approach For this purpose, an eye-tracking experiment was conducted showing participants search interfaces with snippets of one, three or five lines on a mobile device to analyze 17 dependent variables. In total, 31 participants took part in the study. Each of the participants solved informational and navigational tasks. Findings Results indicate a strong influence of page fold on scrolling behavior and attention distribution across search results. Regardless of query type, short snippets seem to provide too little information about the result, so that search performance and subjective measures are negatively affected. Long snippets of five lines lead to better performance than medium snippets for navigational queries, but to worse performance for informational queries. Originality/value Although space in mobile search is limited, this study shows that longer snippets improve usability and user experience. It further emphasizes that page fold plays a stronger role in mobile than in desktop search for attention distribution.

Date

20. 1.2015 18:30:22

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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

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Abstract

To improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users' information needs that goes beyond the queries they type in. Adaptive search engines use explicit and implicit feedback indicators to model users or search tasks. In order to create appropriate models, it is essential to understand how users interact with search engines, including the determining factors of their actions. Using eye tracking, we extend this understanding by analyzing the sequences and patterns with which users evaluate query result returned to them when using Google. We find that the query result abstracts are viewed in the order of their ranking in only about one fifth of the cases, and only an average of about three abstracts per result page are viewed at all. We also compare search behavior variability with respect to different classes of users and different classes of search tasks to reveal whether user models or task models may be greater predictors of behavior. We discover that gender and task significantly influence different kinds of search behaviors discussed here. The results are suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow.

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Abstract

Der Suchmaschinen-Gigant macht gemeinsame Sache mit dem erfolgreichsten sozialen Netzwerk im Web: Google stellt Werbung in "My Space" ein - und zahlt dafür 900 Millionen Dollar.

Content

"Die Wellen schlugen hoch, als der Mediengigant Rupert Murdoch mit seiner Firma Fox Interactive für rund 580 Millionen Dollar die Internetseite "My Space" kaufte. Nun zeigt sich: Die Investition hat sich gelohnt. Ein neues Abkommen mit dem Suchmaschinenbetreiber Google sichert der Webseite 900 Millionen Dollar Gesamteinnahmen in den nächsten vier Jahren. Google wird bis zum fahr 2010 "My Space" und andere Seiten der Fox Interactive mit Anzeigen und Internetsuche versorgen - und sticht so Konkurrenten wie Yahoo oder Microsoft aus. "My Space" ist das derzeit erfolgreichste soziale Netzwerk im Internet. Die Teilnehmer legen eigene Seiten an, suchen Kontakte oder laden Musik herunter. Die Plattform bietet Musikern eine Heimstatt, die ihre Werke oft kostenlos veröffentlichen. Die Zahl der registrierten Nutzer hat sich im vergangenen Halbjahr verdoppelt, aktuell sind etwa 100 Millionen überwiegend junge Leute registriert. "Wie weit wir im vergangenen Jahr gekommen sind, ist wirklich bemerkenswert", sagt Peter Chernin, Präsident von Murdochs News Corporation. Google wird auf der Webseite als Werbelieferant fungieren. Reklame bringt der Suchmaschine das meiste Geld. Mit dem Google-Produkt "Adsense" können Werbetreibende Anzeigen buchen, die erscheinen, wenn Surfer nach bestimmten Begriffen suchen - oder auf anderen Webseiten unterwegs sind, deren Inhalt zum Werbethema passt. Google berechnet die Anzeigenpreise danach, wie viele Werbetreibende mit einem Suchbegriff präsentiert sein wollen. Für einen Klick können mehrere Dollar fällig sein. Allein im zweiten Quartal verbuchte Google Werbeeinnahmen von mehr als 2,4 Milliarden Dollar. Für "My Space" kommt die Geschäftsvereinbarung zur rechten Zeit. In den vergangenen Monaten wurde zunehmend kritisiert, das Unternehmen schütze seine jugendliche Kundschaft zu wenig vor sexueller Belästigung oder anderen schädlichen Inhalten. Nachdem Fälle bekannt wurden, in denen sich Erwachsene jugendlichen über die Internetplattform sexuell genähert hatten, reagierte "My Space" mit einer Fernseh-Kampagne für den verantwortungsvollen Umgang Jugendlicher mit den eigenen Daten. Doch das überzeugte nicht alle US-Politiker. Anfang August hat das Repräsentantenhaus mit großer Mehrheit den "Deleting On - line Predators Act" verabschiedet, der öffentlichen Einrichtungen wie Schulen und Bibliotheken verpflichten soll, Zugriff auf "My Space" und ähnliche Seiten einzuschränken. Bevor das Gesetz in Kraft tritt, muss der US-Senat sein Votum abgeben. Google-Manager Eric E. Schmidt versichert, der Konzern werde "My Space" nicht mit Werbung zupflastern: "Wie sich herausgestellt hat, ist es wirkungsvoller, weniger aber dafür bessere Werbung einzubinden." Auch Google hatte in den vergangenen Monaten Kritik einstecken müssen. So nutzen windige Geschäftemacher das "Adsense", um sich Werbeeinnahmen zu erschwindeln oder die Kosten für Konkurrenten in die Höhe zu treiben. Im Juli hatte sich Google vor einem US-Gericht verpflichtet, 90 Millionen Dollar an Werbetreibende zurückzuzahlen. Das Unternehmen kündigte weitere Schritte gegen den Klickbetrug an."

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Abstract

The Internet provides an exceptional testbed for developing algorithms that can improve bowsing and searching large information spaces. Browsing and searching tasks are susceptible to problems of information overload and vocabulary differences. Much of the current research is aimed at the development and refinement of algorithms to improve browsing and searching by addressing these problems. Our research was focused on discovering whether two of the algorithms our research group has developed, a Kohonen algorithm category map for browsing, and an automatically generated concept space algorithm for searching, can help improve browsing and / or searching the Internet. Our results indicate that a Kohonen self-organizing map (SOM)-based algorithm can successfully categorize a large and eclectic Internet information space (the Entertainment subcategory of Yahoo!) into manageable sub-spaces that users can successfully navigate to locate a homepage of interest to them. The SOM algorithm worked best with browsing tasks that were very broad, and in which subjects skipped around between categories. Subjects especially liked the visual and graphical aspects of the map. Subjects who tried to do a directed search, and those that wanted to use the more familiar mental models (alphabetic or hierarchical organization) for browsing, found that the work did not work well. The results from the concept space experiment were especially encouraging. There were no significant differences among the precision measures for the set of documents identified by subject-suggested terms, thesaurus-suggested terms, and the combination of subject- and thesaurus-suggested terms. The recall measures indicated that the combination of subject- and thesaurs-suggested terms exhibited significantly better recall than subject-suggested terms alone. Furthermore, analysis of the homepages indicated that there was limited overlap between the homepages retrieved by the subject-suggested and thesaurus-suggested terms. Since the retrieval homepages for the most part were different, this suggests that a user can enhance a keyword-based search by using an automatically generated concept space. Subejcts especially liked the level of control that they could exert over the search, and the fact that the terms suggested by the thesaurus were 'real' (i.e., orininating in the homepages) and therefore guaranteed to have retrieval success

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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

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Abstract

Previous work an search-engine design has indicated that information-seekers may benefit from being given the opportunity to exploit multiple sources of evidence of document relatedness. Few existing systems, however, give users more than minimal control over the selections that may be made among methods of exploitation. By applying the methods of "document network analysis" (DNA), a unifying, graph-theoretic model of content-, collaboration-, and context-based systems (CCC) may be developed in which the nature of the similarities between types of document relatedness and document ranking are clarified. The usefulness of the approach to system design suggested by this model may be tested by constructing and evaluating a prototype system (UCXtra) that allows searchers to maintain control over the multiple ways in which document collections may be ranked and re-ranked.

Date

11. 9.2004 17:32:22

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Abstract

Presents a multimedia model which describes the various multimedia components, their structure and their relationships with a pre-defined taxonomy of concepts, in order to support search engine information retrieval process

Date

1. 8.1996 22:08:06

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Abstract

This article gives an overview of the design and organisation of DutchESS, a Dutch information subject gateway created as a national collaborative effort of the National Library and a number of academic libraries. The combined centralised and distributed model of DutchESS is discussed, as well as its selection policy, its metadata format, classification scheme and retrieval options. Also some options for future collaboration on an international level are explored

Date

22. 6.2002 19:39:23

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Abstract

"Xchylerjfk" mag You Tube. Die junge Frau, die hinter diesem verqueren Internet-Namen steckt, mag die Website für Amateurvideos "so, wie sie ist". Und so sehr, dass sie nun ganz aufgeregt ein Kurzvideo für You Tube gedreht hat und darin schimpft: "Sie wollen die Welt beherrschen." Gemeint ist Google, der neue Eigentümer von You Tube. Denn die Internet-Suchmaschine Google übernimmt für 1,65 Milliarden Dollar You Tube (übersetzt etwa: deine Glotze), eine Website, die in weniger als zwei Jahren von einer Idee, ausgeheckt beim Abendessen, zum kulturellen Massenphänomen aufstieg. "Wir wollten unabhängig bleiben", begründete der 29 Jahre alte You-Tube-Chef Chad Hurley in der Nacht zum Dienstag gegenüber der Agentur Reuters, warum er und sein Mitbegründer Steve Chen (27) Google den Zuschlag gaben. Google habe Selbstständigkeit versprochen und sein technisches Wissen könne You Tube dem Ziel näher bringen, die unterhaltsamste Video-Seite im Internet zu sein. "Jetzt haben wir mit Google die Ressourcen hinter uns, um diese Vision zu verwirklichen`; sagte Hurley. Hurley verriet nicht, wie viel Geld er und Chen bei der Übernahme einstreichen. Ein dritter Mitbegründer - Jawed Karim - hatte You Tube verlassen, kurz bevor das Jungunternehmen erstmals 3,5 Millionen Dollar Startkapital von Sequoia Capital ergatterte - diese Firma hatte auch Google in der Start-phase mit Kapital versorgt. Laut Wall Street Journal besitzt Sequoia Capital knapp ein Drittel von You Tube. Der Kauf ist die teuerste Neuerwerbung in der achtjährigen Firmengeschichte und sichert Google die führende Rolle im wachsenden Online-Markt für Videos. Schräge Videoclips boomen ebenso im Internet wie so genannte Social Networks, etwa die Kennenlern-Plattform My Space. Etablierte Medien und Internetfirmen wollen ein Stück vom Kuchen.
Goldgräber-Stimmung Erst im August hatte Google und My Space einen Deal für 900 Millionen Dollar abgeschlossen, der Google erlaubt, auf der Community Website Werbung zu verkaufen. Als Interessenten für You Tube waren Gerüchten zufolge auch Internetgiganten wie Yahoo und Ebay im Gespräch. Ausgelöst hat die jüngste Goldgräber-Stimmung Robert Murdoch, dessen News corporation im vergangenen Jahr My Space für 580 Millionen Dollar erstand - in bar. Was damals eine ungeheure Summe erschien, sieht nach dem Deal mit Google wie ein Schnäppchen aus. Analysten schätzen den Wert von My Space bereits auf zwei Milliarden Dollar. Gründe für den Kauf gibt es genug. Da sind zum einen die "Eyeballs", die Zahl der Nutzer von You Tube: 72 Millionen monatlich. Auch wollte Google angeblich nicht den gleichen Fehler wie vergangenes Jahr machen, als es sich My Space entgehen ließ. Drittens ist die YouTube-Gemeinde nach Untersuchungen aktiver, enthusiastischer, kommentar- und austauschfreudiger als die User bei der Google-Konkurrenz. Hinzu kommt "stickiness". You-Tube-Fans verweilen länger auf der Seite als in vergleichbaren Fällen. Ein wichtiger Aspekt für Werbedollars, mit denen Google sein Geschäft macht. Es ist eine einfache Rechnung: Dieses Jahr werden in den USA schätzungsweise 16 Milliarden Dollar in die Internet-Werbung fließen, 28 Prozent mehr als 2005. Dennoch gibt es Kritiker. Probleme könnten die Urheberrechte machen, denn viele You-Tube-Nutzer laden Ausschnitte aus Fernsehsendungen, Musikvideos und Filmen hoch, die geschützt sind. Erst Stunden, vor Bekanntgabe der Übernahme gaben Google und You Tube eine Reihe von Vertriebsverträgen mit Warner Musik, Sony BMG und Universal bekannt.

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Abstract

This article presents a semantic-based Web retrieval system that is capable of retrieving the Web pages that are conceptually related to the implicit concepts of the query. The concept of concept is managed from a fuzzy point of view by means of semantic areas. In this context, the proposed system improves most search engines that are based on matching words. The key of the system is to use a new version of the Fuzzy Interrelations and Synonymy-Based Concept Representation Model (FIS-CRM) to extract and represent the concepts contained in both the Web pages and the user query. This model, which was integrated into other tools such as the Fuzzy Interrelations and Synonymy based Searcher (FISS) metasearcher and the fz-mail system, considers the fuzzy synonymy and the fuzzy generality interrelations as a means of representing word interrelations (stored in a fuzzy synonymy dictionary and ontologies). The new version of the model, which is based on the study of the cooccurrences of synonyms, integrates a soft method for disambiguating word senses. This method also considers the context of the word to be disambiguated and the thematic ontologies and sets of synonyms stored in the dictionary.

Date

22. 7.2006 17:14:12

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Abstract

This paper presents an application of the model described in Part I to the evaluation of Web search engines by undergraduates. The study observed how 36 undergraduate used four major search engines to find information for their own individual problems and how they evaluated these engines based an actual interaction with the search engines. User evaluation was based an 16 performance measures representing five evaluation criteria: relevance, efficiency, utility, user satisfaction, and connectivity. Non-performance (user-related) measures were also applied. Each participant searched his/ her own topic an all four engines and provided satisfaction ratings for system features and interaction and reasons for satisfaction. Each also made relevance judgements of retrieved items in relation to his/her own information need and participated in post-search Interviews to provide reactions to the search results and overall performance. The study found significant differences in precision PR1 relative recall, user satisfaction with output display, time saving, value of search results, and overall performance among the four engines and also significant engine by discipline interactions an all these measures. In addition, the study found significant differences in user satisfaction with response time among four engines, and significant engine by discipline interaction in user satisfaction with search interface. None of the four search engines dominated in every aspect of the multidimensional evaluation. Content analysis of verbal data identified a number of user criteria and users evaluative comments based an these criteria. Results from both quantitative analysis and content analysis provide insight for system design and development, and useful feedback an strengths and weaknesses of search engines for system improvement

Date

24. 1.2004 18:27:22

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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