Search (9295 results, page 1 of 465)

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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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Theme

Internet

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Abstract

Develops frameworks to help Internet media designers address end user information presentation preferences by advancing structures for assessing metadata design variables. Design variables are then linked to user cognitive styles. An underlying theme is that artificial intelligence methodologies may be used to help automate the Internet media design process and to provide personalized and customized experiences. User preferences concerning knowledge acquisition in online experiences provide the basis for discussions of cognitive analysis, and are extended into structural implications for media design and interaction

Source

Internet research. Electronic networking applications and policy. 8(1998) no.4, S.347-358

Theme

Internet

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Abstract

Purpose - With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach - The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings - Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level. Originality/value - Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

Date

20. 1.2015 18:30:22

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Date

1. 8.1996 22:08:06

Theme

Internet

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Abstract

Library OPACs have long been the gateways between users and information. They present to users the achievements of library automation, and are the most widely available automated retrieval systems and the first that many user encounter. Current trends in OPAC design are toward a user oriented, individual information service which can meet the different needs of users with a variety of background and interests. Compared with the rather inactive, short term and general information service of conventional systems, this type of system focuses on active, long term and personalized service. Proposes a framework for the design of such an OPAC and discusses some recent developments in personalized information service

Date

4. 8.1998 19:36:22

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Abstract

Most on-line news sources are electronic versions of "ink-on-paper" newspapers. These are versions that have been filtered, from the mass of news produced each day, by an editorial board with a given community profile in mind. As readers, we choose the filter rather than choose the stories. New technology, however, provides the potential for personalized versions to be filtered automatically from this mass of news on the basis of user profiles. People read the news for many reasons: to find out "what's going on," to be knowledgeable members of a community, and because the activity itself is pleasurable. Given this, we ask the question, "How much filtering is acceptable to readers?" In this study, an evaluation of user preference for personal editions versus community editions of on-line news was performed. A personalized edition of a local newspaper was created for each subject based on an elliptical model that combined the user profile and community profile as represented by the full edition of the local newspaper. The amount of emphasis given the user profile and the community profile was varied to test the subjects' reactions to different amounts of personalized filtering. The task was simply, "read the news," rather than any subject specific information retrieval task. The results indicate that users prefer the coarse-grained community filters to fine-grained personalized filters

Theme

Internet

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Abstract

A novel idea of media agent is briefly presented, which can automatically build a personalized semantic index of Web media objects for each particular user. Because the Web is a rich source of multimedia data and the text content on the Web pages is usually semantically related to those media objects on the same pages, the media agent can automatically collect the URLs and related text, and then build the index of the multimedia data, on behalf of the user whenever and wherever she accesses these multimedia data or their container Web pages. Moreover, the media agent can also use an off-line crawler to build the index for those multimedia objects that are relevant to the user's favorites but have not accessed by the user yet. When the user wants to find these multimedia data once again, the semantic index facilitates text-based search for her.

Theme

Internet

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Abstract

Over the past few years the amount of information available on the WWW has grown so much and so fast that there is no accurate way to count or estimate it. Directory services and search tools do not learn about the searcher nor adapt themselves to his needs. Draws attention to a new generation of WWW sites that can be customized to the needs of users and offers examples of some of these personalized services

Theme

Internet

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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

Reviews 3 WWW programmes that help deal with information overload. Farcast is a personalized new service in which all commands are delivered and information retrieval via email. It uses droids or electronic agents to search the full text of newsfeeds and send summaries. Quarterdeck's WebCompass is a search manager that employs multiple resources to search the Internet for relevant material based on user-defined queires. It generates summaries based on the most significant sentences in the entire document. Search results are displayed on a multi-pane interface. ForeFront Group's Web-Whacker automatically searches user-defined Web sites, organises them into categories and downloads them into a database that resides on the user's computer. The sites can then be browsed off-line

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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

Theme

Internet

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Abstract

This paper deals with the problem of modeling Web information resources using expert knowledge and personalized user information for improved Web searching capabilities. We propose a "Web information space" model, which is composed of Web-based information resources (HTML/XML [Hypertext Markup Language/Extensible Markup Language] documents an the Web), expert advice repositories (domain-expert-specified metadata for information resources), and personalized information about users (captured as user profiles that indicate users' preferences about experts as well as users' knowledge about topics). Expert advice, the heart of the Web information space model, is specified using topics and relationships among topics (called metalinks), along the lines of the recently proposed topic maps. Topics and metalinks constitute metadata that describe the contents of the underlying HTML/XML Web resources. The metadata specification process is semiautomated, and it exploits XML DTDs (Document Type Definition) to allow domain-expert guided mapping of DTD elements to topics and metalinks. The expert advice is stored in an object-relational database management system (DBMS). To demonstrate the practicality and usability of the proposed Web information space model, we created a prototype expert advice repository of more than one million topics/metalinks for DBLP (Database and Logic Programming) Bibliography data set. We also present a query interface that provides sophisticated querying fa cilities for DBLP Bibliography resources using the expert advice repository.

Theme

Internet

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Abstract

With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink.

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Abstract

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.

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Abstract

This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

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Abstract

Electronic news has long held the promise of personalized and dynamic delivery of current event new items, particularly for Web users. Although wlwctronic versions of print news are now widely available, the personalization of that delivery has not yet been accomplished. In this paper, we present a methodology of associating news documents based on the extraction of feature phrases, where feature phrases identify dates, locations, people and organizations. A news representation is created from these feature phrases to define news objects that can then be compared and ranked to find related news items. Unlike tradtional information retrieval, we are much more interested in precision than recall. That is, the user would like to see one or more specifically related articles, rather than all somewhat related articles. The algorithm is designed to work interactively the the user using regular web browsers as the interface

Theme

Internet

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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.

Theme

Internet

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Abstract

The accurate suggestion of interesting friends arises as a crucial issue in recommendation systems. The selection of friends or followees responds to several reasons whose importance might differ according to the characteristics and preferences of each user. Furthermore, those preferences might also change over time. Consequently, understanding how friends or followees are selected emerges as a key design factor of strategies for personalized recommendations. In this work, we argue that the criteria for recommending followees needs to be adapted and combined according to each user's behavior, preferences, and characteristics. A method is proposed for adapting such criteria to the characteristics of the previously selected followees. Moreover, the criteria can evolve over time to adapt to changes in user behavior, and broaden the diversity of the recommendation of potential followees based on novelty. Experimental evaluation showed that the proposed method improved precision results regarding static criteria weighting strategies and traditional rank aggregation techniques.

Theme

Internet

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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.