Search (38 results, page 1 of 2)

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Abstract

Social Search beschreibt die Einbeziehung des sozialen Kontexts in den Prozess der Informationssuche. Der folgende Beitrag zeigt die Entwicklung sozialer Suche auf und systematisiert den Bereich von Social Search Engines im WWW durch eine grundlegende Unterscheidung verschiedener Social-SearchKonzepte. Durch die Beschreibung typischer Merkmale bestehender Social-Search-Implementierungen werden diese überschau- und vergleichbar gemacht.

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Abstract

This chapter shows that the wider use of Web search engines, reconsidering the theoretical and methodological frameworks to grasp new information practices. Beginning with an overview of the recent challenges implied by the dynamic nature of the Web, this chapter then traces the information behavior related concepts in order to present the different approaches from the user perspective. The authors pay special attention to the concept of "information practice" and other related concepts such as "use", "activity", and "behavior" largely used in the literature but not always strictly defined. The authors provide an overview of user-oriented studies that are meaningful to understand the different contexts of use of electronic information access systems, focusing on five approaches: the system-oriented approaches, the theories of information seeking, the cognitive and psychological approaches, the management science approaches, and the marketing approaches. Future directions of work are then shaped, including social searching and the ethical, cultural, and political dimensions of Web search engines. The authors conclude considering the importance of Critical theory to better understand the role of Web Search engines in our modern society.

Date

20. 4.2012 13:22:37

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Abstract

Folksonomies ermöglichen den Nutzern in Kollaborativen Informationsdiensten den Zugang zu verschiedenartigen Informationsressourcen. In welchen Fällen beide Bestandteile des Web 2.0 am besten für das Information Retrieval geeignet sind und wo sie die Websuche ggf. ersetzen können, wird in diesem Beitrag diskutiert. Dazu erfolgt eine detaillierte Betrachtung der Reichweite von Social-Bookmarking-Systemen und Sharing-Systemen sowie der Retrievaleffektivität von Folksonomies innerhalb von Kollaborativen Informationsdiensten.

Theme

Social tagging

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Abstract

Despite improvements in their capabilities, search engines still fail to provide users with only relevant results. One reason is that most search engines implement a "one size fits all" approach that ignores personal preferences when retrieving the results of a user's query. Recent studies (Smyth, 2010) have elaborated the importance of personalizing search results and have proposed integrating recommender system methods for enhancing results using contextual and extrinsic information that might indicate the user's actual needs. In this article, we review recommender system methods used for personalizing and improving search results and examine the effect of two such methods that are merged for this purpose. One method is based on collaborative users' knowledge; the second integrates information from the user's social network. We propose new methods for collaborative-and social-based search and demonstrate that each of these methods, when separately applied, produce more accurate search results than does a purely keyword-based search engine (referred to as "standard search engine"), where the social search engine is more accurate than is the collaborative one. However, separately applied, these methods do not produce a sufficient number of results (low coverage). Nevertheless, merging these methods with those implemented by standard search engines overcomes the low-coverage problem and produces personalized results for users that display significantly more accurate results while also providing sufficient coverage than do standard search engines. The improvement, however, is significant only for topics for which the diversity of terms used for queries among users is low.

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Content

Inhalt: Acknowledgments; Abstract; Zusammenfassung; Contents; List of Figures; List of Tables; List of Acronyms; Chapter 1 Introduction ; 1.1 Research Questions; 1.2 Thesis Outline; Part I Fundamentals ; Chapter 2 Information Retrieval for Young Users ; 2.1 Basics of Information Retrieval; 2.1.1 Architecture of an IR System; 2.1.2 Relevance Ranking; 2.1.3 Search User Interfaces; 2.1.4 Targeted Search Engines; 2.2 Aspects of Child Development Relevant for Information Retrieval Tasks; 2.2.1 Human Cognitive Development; 2.2.2 Information Processing Theory; 2.2.3 Psychosocial Development 2.3 User Studies and Evaluation2.3.1 Methods in User Studies; 2.3.2 Types of Evaluation; 2.3.3 Evaluation with Children; 2.4 Discussion; Chapter 3 State of the Art ; 3.1 Children's Information-Seeking Behaviour; 3.1.1 Querying Behaviour; 3.1.2 Search Strategy; 3.1.3 Navigation Style; 3.1.4 User Interface; 3.1.5 Relevance Judgement; 3.2 Existing Algorithms and User Interface Concepts for Children; 3.2.1 Query; 3.2.2 Content; 3.2.3 Ranking; 3.2.4 Search Result Visualisation; 3.3 Existing Information Retrieval Systems for Children; 3.3.1 Digital Book Libraries; 3.3.2 Web Search Engines 3.4 Summary and DiscussionPart II Studying Open Issues ; Chapter 4 Usability of Existing Search Engines for Young Users ; 4.1 Assessment Criteria; 4.1.1 Criteria for Matching the Motor Skills; 4.1.2 Criteria for Matching the Cognitive Skills; 4.2 Results; 4.2.1 Conformance with Motor Skills; 4.2.2 Conformance with the Cognitive Skills; 4.2.3 Presentation of Search Results; 4.2.4 Browsing versus Searching; 4.2.5 Navigational Style; 4.3 Summary and Discussion; Chapter 5 Large-scale Analysis of Children's Queries and Search Interactions; 5.1 Dataset; 5.2 Results; 5.3 Summary and Discussion Chapter 6 Differences in Usability and Perception of Targeted Web Search Engines between Children and Adults 6.1 Related Work; 6.2 User Study; 6.3 Study Results; 6.4 Summary and Discussion; Part III Tackling the Challenges ; Chapter 7 Search User Interface Design for Children ; 7.1 Conceptual Challenges and Possible Solutions; 7.2 Knowledge Journey Design; 7.3 Evaluation; 7.3.1 Study Design; 7.3.2 Study Results; 7.4 Voice-Controlled Search: Initial Study; 7.4.1 User Study; 7.5 Summary and Discussion; Chapter 8 Addressing User Diversity ; 8.1 Evolving Search User Interface 8.1.1 Mapping Function8.1.2 Evolving Skills; 8.1.3 Detection of User Abilities; 8.1.4 Design Concepts; 8.2 Adaptation of a Search User Interface towards User Needs; 8.2.1 Design & Implementation; 8.2.2 Search Input; 8.2.3 Result Output; 8.2.4 General Properties; 8.2.5 Configuration and Further Details; 8.3 Evaluation; 8.3.1 Study Design; 8.3.2 Study Results; 8.3.3 Preferred UI Settings; 8.3.4 User satisfaction; 8.4 Knowledge Journey Exhibit; 8.4.1 Hardware; 8.4.2 Frontend; 8.4.3 Backend; 8.5 Summary and Discussion; Chapter 9 Supporting Visual Searchers in Processing Search Results 9.1 Related Work

Date

1. 2.2016 18:25:22

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Issue

Teil 2: Suchwortvermarktung, Suchmaschinenoptimierung, Social Media Marketing, Mobile Marketing/lokationsbasiertes Marketing [04.10.2019].

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Abstract

"Web Search Engine Research", edited by Dirk Lewandowski, provides an understanding of Web search engines from the unique perspective of Library and Information Science. The book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, reliability of search results, query log analysis, user guidance in the search process, and the influence of search engine optimization (SEO) on results quality. While research in computer science has mainly focused on technical aspects of search engines, LIS research is centred on users' behaviour when using search engines and how this interaction can be evaluated. LIS research provides a unique perspective in intermediating between the technical aspects, user aspects and their impact on their role in knowledge acquisition. This book is directly relevant to researchers and practitioners in library and information science, computer science, including Web researchers.

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Abstract

Recent technological progress in computer science, Web technologies, and constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Web search has significantly evolved in recent years. In the beginning, web search engines such as Google and Yahoo! were only providing search service over text documents. Aggregated search was one of the first steps to go beyond text search, and was the beginning of a new era for information seeking and retrieval. These days, new web search engines support aggregated search over a number of vertices, and blend different types of documents (e.g., images, videos) in their search results. New search engines employ advanced techniques involving machine learning, computational linguistics and psychology, user interaction and modeling, information visualization, Web engineering, artificial intelligence, distributed systems, social networks, statistical analysis, semantic analysis, and technologies over query sessions. Documents no longer exist on their own; they are connected to other documents, they are associated with users and their position in a social network, and they can be mapped onto a variety of ontologies. Similarly, retrieval tasks have become more interactive and are solidly embedded in a user's geospatial, social, and historical context. It is conjectured that new breakthroughs in information retrieval will not come from smarter algorithms that better exploit existing information sources, but from new retrieval algorithms that can intelligently use and combine new sources of contextual metadata.
With the rapid growth of web-based applications, such as search engines, Facebook, and Twitter, the development of effective and personalized information retrieval techniques and of user interfaces is essential. The amount of shared information and of social networks has also considerably grown, requiring metadata for new sources of information, like Wikipedia and ODP. These metadata have to provide classification information for a wide range of topics, as well as for social networking sites like Twitter, and Facebook, each of which provides additional preferences, tagging information and social contexts. Due to the explosion of social networks and other metadata sources, it is an opportune time to identify ways to exploit such metadata in IR tasks such as user modeling, query understanding, and personalization, to name a few. Although the use of traditional metadata such as html text, web page titles, and anchor text is fairly well-understood, the use of category information, user behavior data, and geographical information is just beginning to be studied. This book is intended for scientists and decision-makers who wish to gain working knowledge about search engines in order to evaluate available solutions and to dialogue with software and data providers.

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Abstract

Exploiting context information in a web search engine helps fine-tuning web services and applications to deliver custom-made information to end users. While context, including user and environment information, cannot be exploited efficiently in the wired Internet interaction type, it is becoming accessible with the mobile web where users have an intimate relationship with their handsets. In this type of interaction, context plays a significant role enhancing information search and therefore, allowing a search engine to detect relevant content in all digital forms and formats. This chapter proposes a context model and an architecture that promote integration of context information for individuals and social communities to add value to their interaction with the mobile web. The architecture relies on efficient knowledge management of multimedia resources for a wide range of applications and web services. The research is illustrated with a corporate case study showing how efficient context integration improves usability of a mobile search engine.

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Abstract

Over the last years, research and industry players have become increasingly interested in analyzing opinions and sentiments expressed on the social media web for product marketing and business intelligence. In order to adapt to this need search engines not only have to be able to retrieve lists of documents but to directly access, analyze, and interpret topics and opinions. This article covers an intermediate phase of the ongoing industrial research project 'DoXa' aiming at developing a semantic opinion and sentiment mining search engine for the French language. The DoXa search engine enables topic related opinion and sentiment extraction beyond positive and negative polarity using rich linguistic resources. Centering the work on two distinct business use cases, the authors analyze both unstructured Web 2.0 contents (e.g., blogs and forums) and structured questionnaire data sets. The focus is on discovering hidden patterns in the data. To this end, the authors present work in progress on opinion topic relation extraction and visual analytics, linguistic resource construction as well as the combination of OLAP technology with semantic search.

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Content

Titel der einzelnen Kapitel: Social Search Folksonomies und Kollaborative Informationsdienste: Eine Alternative zur Websuche? Query Understanding Semantic Search Suche in Multimediaarchiven und Kultureinrichtungen Wissenschaftliche Suchmaschinen Journalistische Recherche im Internet Evaluierung von Suchmaschinen Usability und User Experience in Suchmaschinen Search Engine Bias Web Monitoring Tools für das Monitoring

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Abstract

Web search engines act as gatekeepers when people search for information online. Research has shown that search engine users seem to trust the search engines' ranking uncritically and mostly select top-ranked results. This study further examines search engine users' selection behavior. Drawing from the credibility and information research literature, we test whether the presence or absence of certain credibility cues influences the selection probability of search engine results. In an observational study, participants (N?=?247) completed two information research tasks on preset search engine results pages, on which three credibility cues (source reputation, message neutrality, and social recommendations) as well as the search result ranking were systematically varied. The results of our study confirm the significance of the ranking. Of the three credibility cues, only reputation had an additional effect on selection probabilities. Personal characteristics (prior knowledge about the researched issues, search engine usage patterns, etc.) did not influence the preference for search results linked with certain credibility cues. These findings are discussed in light of situational and contextual characteristics (e.g., involvement, low-cost scenarios).

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Abstract

In der heutigen Zeit nimmt die Flut an Informationen exponentiell zu. In dieser »Informationsexplosion« entsteht täglich eine unüberschaubare Menge an neuen Informationen im Web: Beispielsweise 430 deutschsprachige Artikel bei Wikipedia, 2,4 Mio. Tweets bei Twitter und 12,2 Mio. Kommentare bei Facebook. Während in Deutschland vor einigen Jahren noch Google als nahezu einzige Suchmaschine beim Zugriff auf Informationen im Web genutzt wurde, nehmen heute die u.a. in Social Media veröffentlichten Meinungen und damit die Vorauswahl sowie Bewertung von Informationen einzelner Experten und Meinungsführer an Bedeutung zu. Aber wie können themenspezifische Informationen nun effizient für konkrete Fragestellungen identifiziert und bedarfsgerecht aufbereitet und visualisiert werden? Diese Studie gibt einen Überblick über semantische Standards und Formate, die Prozesse der semantischen Suche, Methoden und Techniken semantischer Suchsysteme, Komponenten zur Entwicklung semantischer Suchmaschinen sowie den Aufbau bestehender Anwendungen. Die Studie erläutert den prinzipiellen Aufbau semantischer Suchsysteme und stellt Methoden der semantischen Suche vor. Zudem werden Softwarewerkzeuge vorgestellt, mithilfe derer einzelne Funktionalitäten von semantischen Suchmaschinen realisiert werden können. Abschließend erfolgt die Betrachtung bestehender semantischer Suchmaschinen zur Veranschaulichung der Unterschiede der Systeme im Aufbau sowie in der Funktionalität.

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Abstract

Few companies in history have ever been as successful and as admired as Google, the company that has transformed the Internet and become an indispensable part of our lives. How has Google done it? Veteran technology reporter Steven Levy was granted unprecedented access to the company, and in this revelatory book he takes readers inside Google headquarters-the Googleplex-to show how Google works. While they were still students at Stanford, Google cofounders Larry Page and Sergey Brin revolutionized Internet search. They followed this brilliant innovation with another, as two of Google's earliest employees found a way to do what no one else had: make billions of dollars from Internet advertising. With this cash cow (until Google's IPO nobody other than Google management had any idea how lucrative the company's ad business was), Google was able to expand dramatically and take on other transformative projects: more efficient data centers, open-source cell phones, free Internet video (YouTube), cloud computing, digitizing books, and much more. The key to Google's success in all these businesses, Levy reveals, is its engineering mind-set and adoption of such Internet values as speed, openness, experimentation, and risk taking. After its unapologetically elitist approach to hiring, Google pampers its engineers-free food and dry cleaning, on-site doctors and masseuses-and gives them all the resources they need to succeed. Even today, with a workforce of more than 23,000, Larry Page signs off on every hire. But has Google lost its innovative edge? It stumbled badly in China-Levy discloses what went wrong and how Brin disagreed with his peers on the China strategy-and now with its newest initiative, social networking, Google is chasing a successful competitor for the first time. Some employees are leaving the company for smaller, nimbler start-ups. Can the company that famously decided not to be evil still compete? No other book has ever turned Google inside out as Levy does with In the Plex.

Content

The world according to Google: biography of a search engine -- Googlenomics: cracking the code on internet profits -- Don't be evil: how Google built its culture -- Google's cloud: how Google built data centers and killed the hard drive -- Outside the box: the Google phone company. and the Google t.v. company -- Guge: Google moral dilemma in China -- Google.gov: is what's good for Google, good for government or the public? -- Epilogue: chasing tail lights: trying to crack the social code.

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Abstract

This paper reports on a transaction log analysis of the type and topic of search queries entered into the search engine Google (Australia). Two aspects, in particular, set this apart from previous studies: the sampling and analysis take account of the distribution of search queries, and lifestyle information of the searcher was matched with each search query. A surprising finding was that there was no observed statistically significant difference in search type or topics for different segments of the online population. It was found that queries about popular culture and Ecommerce accounted for almost half of all search engine queries and that half of the queries were entered with a particular Website in mind. The findings of this study also suggest that the Internet search engine is not only an interface to information or a shortcut to Websites, it is equally a site of leisure. This study has implications for the design and evaluation of search engines as well as our understanding of search engine use.

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Abstract

Information Retrieval (IR) research typically evaluates search systems in terms of the standard precision, recall and F-measures to weight the relative importance of precision and recall (e.g. van Rijsbergen, 1979). All of these assess the extent to which the system returns good matches for a query. In contrast, webometric measures are designed specifically for web search engines and are designed to monitor changes in results over time and various aspects of the internal logic of the way in which search engine select the results to be returned. This chapter introduces a range of webometric measurements and illustrates them with case studies of Google, Bing and Yahoo! This is a very fertile area for simple and complex new investigations into search engine results.

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Content

Wer bei horizobu, das in einer englischsprachigen und einer deutschsprachigen Version (für die Schweiz) angeboten wird, nach einem bestimmten Begriff sucht, dem präsentiert der Dienst lediglich sechs möglichst relevante Ergebnisse in voneinander separierten Boxen. Einzelne dieser Boxen lassen sich löschen, wodurch ein weiteres Suchergebnis ans Tageslicht befördert wird. Mit einem Klick auf "More" wechselt man alle sechs Resultate aus. Gefällige Resultate lassen sich per Drag-And-Drop in der rechten Spalte ablegen und für später aufbewahren. Um seinem Entdeckeranspruch gerecht zu werden, listet horizobu oberhalb der Suchergebnisse zehn mit dem jeweiligen Suchwort verwandte Begriffe, die beliebig kombinierbar zur Verfeinerung und Erweiterung der bisherigen Suche verwendet werden können. Inspiriert durch die Vorschläge haben User auch die Möglichkeit, weitere Begriffe zur Eingrenzung hinzufügen. "Die Intelligenz aus Social Software führt teilweise zu überraschenden Fundstücken, die bislang mit herkömmlichen Suchmaschinen kaum erschlossen wurden", so Werner Hartmann, Titularprofessor der ETH Zürich und einer der zwei Gründer von horizobu. Nachdem Nutzer mit den Resultaten ihrer Recherche zufrieden sind und diese womöglich mit der Bookmarking-Funktion gespeichert haben, kann die so entstandene Linksammlung direkt über einen eindeutigen Link weiterversendet werden (das sieht z.B. so aus). Diese Funktion ist zwar nicht revolutionär, aber simpel, intuitiv und erfordert wenige Klicks.

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Abstract

The widespread availability of the Internet and the variety of Internet-based applications have resulted in a significant increase in the amount of web pages. Determining the behaviors of search engine users has become a critical step in enhancing search engine performance. Search engine user behaviors can be determined by content-based or content-ignorant algorithms. Although many content-ignorant studies have been performed to automatically identify new topics, previous results have demonstrated that spelling errors can cause significant errors in topic shift estimates. In this study, we focused on minimizing the number of wrong estimates that were based on spelling errors. We developed a new hybrid algorithm combining character n-gram and neural network methodologies, and compared the experimental results with results from previous studies. For the FAST and Excite datasets, the proposed algorithm improved topic shift estimates by 6.987% and 2.639%, respectively. Moreover, we analyzed the performance of the character n-gram method in different aspects including the comparison with Levenshtein edit-distance method. The experimental results demonstrated that the character n-gram method outperformed to the Levensthein edit distance method in terms of topic identification.

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Date

18. 9.2018 18:22:18

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Date

17.12.2013 11:02:22