Search (154 results, page 1 of 8)

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Abstract

'My Yahoo!' (http://www.my.yahoo.com), 'Apple Personalized Internet Launcher' (http://myhome.apple.com/home/welcome/guest), and 'Your Personal Net' (http://www.ypn.com), are personalized WWW search services that could be useful for selecting Internet resources for the library. Outline the services, how to register and use them and how they could be used in the library

Object

Apple Personalized Internt Launcher

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Abstract

The widely used Web search engines index and recommend individual Web pages in response to a few keywords queries to assist users in locating relevant documents. However, the Web search engines give different users the same answer set, although the users may have different preferences. A personalized Web search would carry out the search for each user according to his or her preferences. To conduct the personalized Web search, the authors provide a novel approach to model the user profile with a self-organizing map (SOM). Their results indicate that SOM is capable of helping the user to find the related category for each query used in the Web search to make a personalized Web search effective.

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Abstract

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

Date

22. 7.2006 16:59:13

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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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Abstract

Describes 4 search engines for the Internet: infoMarket Search; Yahoo and OpenText; Lycos Spider; and WebCompass. InfoMarket Search retrieves data from Web pages and information providers such as Disclosure, Information Access Company and Cambridge Scientific Abstracts. It is able to search millions of Web pages in under five seconds. Automated 'crawlers' index the complete text of Web documents. Yahoo enables users to search for specific words and phrases and conduct multilevel Boolean and weighted searches. Lycos spider offers support for HotJava and indexes 91% of the Web. WebCompass polls multiple search engines such as Lycos and InfoSeek for relevant Web pages. A personalized index of topics may be built and retrieved data stored in a format based on Microsoft Access 2.0

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Abstract

Aufgabe des Wissensmanagements ist es, den Mitarbeitern im Unternehmen entscheidungs- und handlungsrelevante Informationen bereitzustellen und die Mitarbeiter bei der intelligenten Verarbeitung dieser Informationen zu unterstützen. Ein hierzu genutztes Werkzeug von wachsender Bedeutung sind Unternehmensportale. Wir beschreiben kurz die Entwicklung von Portalen im World Wide Web (WWW), um dann Web-Portale von verschiedenen Arten von Unternehmensportalen abzugrenzen. Wir zeigen erwartete Funktionalitäten auf und stellen ein 5-Schichten Modell einer Gesamtarchitektur für Portale dar, welche die wesentlichen Komponenten umfasst. Im Anschluss werden die Besonderheiten der organisatorischen Realisierung und im Ausblick der Übergang von Portalen zum ,ubiquitous personalized information supply", der überall verfügbaren und individuellen Informationsversorgung behandelt

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Abstract

Directed Query Engines, an emerging class of search engine specifically designed to access distributed resources on the deep web, offer the opportunity to create inexpensive digital libraries. Already, one such engine, Distributed Explorer, has been used to select and assemble high quality information resources and incorporate them into publicly available systems for the physical sciences. By nesting Directed Query Engines so that one query launches several other engines in a cascading fashion, enormous virtual collections may soon be assembled to form a comprehensive information infrastructure for the physical sciences. Once a Directed Query Engine has been configured for a set of information resources, distributed alerts tools can provide patrons with personalized, profile-based notices of recent additions to any of the selected resources. Due to the potentially enormous size and scope of Directed Query Engine applications, consideration must be given to issues surrounding the representation of large quantities of information from multiple, heterogeneous sources.

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Abstract

In this study, we investigate the similarities and differences between rankings of search results by users and search engines. Sixty-seven students took part in a 3-week-long experiment, during which they were asked to identify and rank the top 10 documents from the set of URLs that were retrieved by three major search engines (Google, MSN Search, and Yahoo!) for 12 selected queries. The URLs and accompanying snippets were displayed in random order, without disclosing which search engine(s) retrieved any specific URL for the query. We computed the similarity of the rankings of the users and search engines using four nonparametric correlation measures in [0,1] that complement each other. The findings show that the similarities between the users' choices and the rankings of the search engines are low. We examined the effects of the presentation order of the results, and of the thinking styles of the participants. Presentation order influences the rankings, but overall the results indicate that there is no "average user," and even if the users have the same basic knowledge of a topic, they evaluate information in their own context, which is influenced by cognitive, affective, and physical factors. This is the first large-scale experiment in which users were asked to rank the results of identical queries. The analysis of the experimental results demonstrates the potential for personalized search.

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Content

Search engines now need to concentrate on addressing personalized searches by collecting more information on user habits, archive search information, style of search, location, educational and other background information before delivery of content. To satisfy a particular user, search engines need to generate a database of user characteristics (e.g., country, level of understanding of search technology, previous searches, applications he/she may use after the search, utility of the information, and the information life cycle), so that it can improve the quality of the content. More categorization of these characteristics will help delivery of appropriately relevant content. At present, browsers do not discriminate among users based upon any of these characteristics to improve content delivery. These additional features, if present in the browser, would form a good interactive interface between the World Wide Web and the user in the near future. Therefore, browser development should take the direction of technical integration with user characteristics and the constant updating of such information by a learning process associated with the user. This could improve the relevance of the retrieved content, i.e., the result set. The more a user browses, the more a browser would learn about that user. The next generation of browsers should have the capacity for intelligence to address issues related to content filtering, address blocking, user friendliness, and various levels of understanding. These browsers should also possess data mining tools so that the search engines could better facilitate user requirements. In such contexts, the browser would act as a self-reviewed catalyst to accelerate the delivery of quality content."

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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/.

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Abstract

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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Date

7. 2.1996 22:38:41

Pages

22 S

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Date

22. 8.1999 20:22:06

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Date

22. 8.1999 21:22:59

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Date

27.11.2005 18:04:22

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Date

22. 6.2002 19:40:22

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Source

Computers in libraries. 19(1999) no.5, S.22-28

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Date

25. 8.2005 17:42:22

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Date

3. 5.1997 8:44:22

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  0.25 = coord(1/4)

Source

Ariadne. 1999, no.22