Search (34 results, page 1 of 2)

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RSWK

Suchmaschine / Recherche / Suchverfahren / Aufsatzsammlung
Suchmaschine / Benutzer / Informationskompetenz / Aufsatzsammlung
Google / Internet / Information Retrieval / Aufsatzsammlung

Subject

Suchmaschine / Recherche / Suchverfahren / Aufsatzsammlung
Suchmaschine / Benutzer / Informationskompetenz / Aufsatzsammlung
Google / Internet / Information Retrieval / Aufsatzsammlung

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Abstract

Dyslexic users often do not exhibit spelling and reading skills at a level required to perform effective search. To explore whether autocomplete functions reduce the impact of dyslexia on information searching, 20 participants with dyslexia and 20 controls solved 10 predefined tasks in the search engine Google. Eye-tracking and screen-capture documented the searches. There were no significant differences between the dyslexic students and the controls in time usage, number of queries, query lengths, or the use of the autocomplete function. However, participants with dyslexia made more misspellings and looked less at the screen and the autocomplete suggestions lists while entering the queries. The results indicate that although the autocomplete function supported the participants in the search process, a more extensive use of the autocomplete function would have reduced misspellings. Further, the high tolerance for spelling errors considerably reduced the effect of dyslexia, and may be as important as the autocomplete function.

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Abstract

The doctoral thesis of Tatiana Gossen formulates criteria and guidelines on how to design the user interfaces of search engines for children. In her work, the author identifies the conceptual challenges based on own and previous user studies and addresses the changing characteristics of the users by providing a means of adaptation. Additionally, a novel type of search result visualisation for children with cartoon style characters is developed taking children's preference for visual information into account.

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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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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Content

Vgl.: https://yis.univie.ac.at/index.php/yis/article/view/1355. Diesem Beitrag liegt folgende Abschlussarbeit zugrunde: Günther, Markus: Welches Weltbild vermitteln Suchmaschinen? Untersuchung der Gewichtung inhaltlicher Aspekte von Google- und Bing-Ergebnissen in Deutschland und den USA zu aktuellen internationalen Themen . Masterarbeit (M.A.), Hochschule für Angewandte Wissenschaften Hamburg, 2015. Volltext: http://edoc.sub.uni-hamburg.de/haw/volltexte/2016/332.

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RSWK

Suchmaschine / Technik / Gesellschaft / Aufsatzsammlung

Subject

Suchmaschine / Technik / Gesellschaft / Aufsatzsammlung

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RSWK

Internet / Suchmaschine / Forschung / Aufsatzsammlung

Subject

Internet / Suchmaschine / Forschung / Aufsatzsammlung

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Footnote

This research was supported by the LMUexcellent Junior Researcher Fund. The authors would like to thank Nora Denner, Lena Storck, and especially Ramona Berger for their assistance with experiment supervision and coding, and the reviewers for their valuable suggestions.

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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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Abstract

Previous work has established that search engine queries can be classified according to the intent of the searcher (i.e., why is the user searching, what specifically do they intend to do). In this article, we describe an experiment in which four sets of queries, each set representing a different user intent, are repeatedly submitted to three search engines over a period of 60 days. Using a variety of measurements, we describe the overall stability of the search engine results recorded for each group. Our findings suggest that search engine results for informational queries are significantly more stable than the results obtained using transactional, navigational, or commercial queries.

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Abstract

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.

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Abstract

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement.

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Abstract

Search engines are essential tools for web users today. They rely on a large number of features to compute the rank of search results for each given query. The estimated reputation of pages is among the effective features available for search engine designers, probably being adopted by most current commercial search engines. Page reputation is estimated by analyzing the linkage relationships between pages. This information is used by link analysis algorithms as a query-independent feature, to be taken into account when computing the rank of the results. Unfortunately, several types of links found on the web may damage the estimated page reputation and thus cause a negative effect on the quality of search results. This work studies alternatives to reduce the negative impact of such noisy links. More specifically, the authors propose and evaluate new methods that deal with noisy links, considering scenarios where the reputation of pages is computed using the PageRank algorithm. They show, through experiments with real web content, that their methods achieve significant improvements when compared to previous solutions proposed in the literature.

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Abstract

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files.

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Abstract

A non-negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no-answer queries, we devised a large number of features that can be used to identify such queries and built machine-learning models. These models can be useful for scenarios such as the mobile- or meta-search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.

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Date

18. 9.2018 18:22:18

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Date

17.12.2013 11:02:22

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