Search (1319 results, page 1 of 66)

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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

Bioinformatics is a multifaceted discipline combining many scientific fields including computational biology, statistics, mathematics, molecular biology, and genetics. Bioinformatics enables biomedical investigators to exploit existing and emerging computational technologies to seamlessly store, mine, retrieve, and analyze data from genomics and proteomics technologies. This is achieved by creating unified data models, standardizing data interfaces, developing structured vocabularies, generating new data visualization methods, and capturing detailed metadata that describes various aspects of the experimental design and analysis methods. Already there are a number of related undertakings that are dividing the field into more specialized groups. Clinical Bioinformatics and Biomedical Informatics are emerging as transitional fields to promote the utilization of genomics and proteomics data combined with medical history and demographic data towards personalized medicine, molecular diagnostics, pharmacogenomics and predicting outcomes of therapeutic interventions. The field of bioinformatics will continue to evolve through the incorporation of diverse technologies and methodologies that draw experts from disparate fields to create the latest computational and informational tools specifically design for the biomedical research enterprise.

Date

22. 7.2006 14:21:27

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Abstract

The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.

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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

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

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Abstract

The Web, and consequently the information contained in it, is growing rapidly. Every day a huge amount of newly created information is electronically published in Digital Libraries, whose aim is to satisfy users' information needs. In this paper, we envisage a Digital Library not only as an information resource where users may submit queries to satisfy their daily information need, but also as a collaborative working and meeting space of people sharing common interests. Indeed, we will present a personalized collaborative Digital Library environment, where users may organize the information space according to their own subjective view, may build communities, may become aware of each other, may exchange information and knowledge with other users, and may get recommendations based on preference patterns of users.

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Abstract

ADEPT is a 5-year project whose goals are to develop, deploy, and evaluate inquiry learning capabilities for the Alexandria Digital Library, an extant digital library of primary sources in geography. We interviewed nine geography faculty members who teach undergraduate courses about their information seeking for research and teaching and their use of information resources in teaching. These data were supplemented by interviews with four faculty members from another ADEPT study about the nature of knowledge in geography. Among our key findings are that geography faculty are more likely to encounter useful teaching resources while seeking research resources than vice versa, although the influence goes in both directions. Their greatest information needs are for research data, maps, and images. They desire better searching by concept or theme, in addition to searching by location and place name. They make extensive use of their own research resources in their teaching. Among the implications for functionality and architecture of geographic digital libraries for educational use are that personal digital libraries are essential, because individual faculty members have personalized approaches to selecting, collecting, and organizing teaching resources. Digital library services for research and teaching should include the ability to import content from common office software and to store content in standard formats that can be exported to other applications. Digital library services can facilitate sharing among faculty but cannot overcome barriers such as intellectual property rights, access to proprietary research data, or the desire of individuals to maintain control over their own resources. Faculty use of primary and secondary resources needs to be better understood if we are to design successful digital libraries for research and teaching.

Date

3. 6.2005 20:40:22

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Abstract

The wide acceptance of social networking tools in online environments is prompting scientists to engage in metadata creation in not only for organizing their own digital records, but also for contributing to data and journal repositories. Understanding the behaviors and practices of these communities can help us create more effective metadata structures within our information systems. This point is underscored by information science researchers who have emphasized the need to examine how certain communities interact with, search for, or organize information (Palmer 2001). By examining scientists, information professionals can be more informed in how to create better collections, services, and systems. As library and repository collections become more diverse and personalized, the organization and ingest techniques/applications behind those systems also should be based on observations of how actual user communities work. One area that is relevant to the practice of scientists and metadata is personal information management (PIM). The study of personal Information management typically focuses on finding (a relative of retrieval), refinding, maintenance, and organization. Metadata is at the core of these activities, although current research seems to focus more on task completion, rather than the underlying metadata structures and arrangements. Most PIM studies and writings have focused on tool development and finding (Jones 2007), but have rarely look closely at the organizational/metadata practices of individuals. As scientific communities, like evolutionary biology, turn more to cyberinfrastructures for sharing and collaborating with each other, it is important for information professionals to understand the more personal aspects of metadata generation and organization. Recent studies done by the Dryad repository69 team have looked at different aspects of data sharing and reuse in the evolutionary biology community. These studies have prompted questions about metadatageneration by scientists, their perceptions of the process, and the link between their metadata and the structures imposed in information systems. This poster will report on a study examining how evolutionary biologists create and use personal metadata to organize their research data. Using an ethnographic interview technique, participants are being interviewed about their current and previous data organization styles and techniques. This information about metadata and information organization can be used to inform new workflow and organization models for knowledge organization and metadata creation practices in developments for repositories, libraries, and cyberinfrastructures.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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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.

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Abstract

In this article, we introduce a user preference model contained in the User Interaction Tools Clause of the MPEG-7 Multimedia Description Schemes, which is described by a UserPreferences description scheme (DS) and a UsageHistory description scheme (DS). Then we propose a user preference learning algorithm by using a Bayesian network to which weighted usage history data on multimedia consumption is taken as input. Our user preference learning algorithm adopts a dynamic learning method for learning real-time changes in a user's preferences from content consumption history data by weighting these choices in time. Finally, we address a user preference-based television program recommendation system on the basis of the user preference learning algorithm and show experimental results for a large set of realistic usage-history data of watched television programs. The experimental results suggest that our automatic user reference learning method is well suited for a personalized electronic program guide (EPG) application.

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Abstract

This paper describes ePaper, a research prototype system of a personalized newspaper on a mobile reading device. The ePaper aggregates content (i.e., news items) from various news providers, classifies the news items according to concepts from a news domain ontology, and delivers an electronic newspaper to each subscribed user (reader). The system personalizes the content of the newspaper according to the user's profiles and preferences by applying ontological content-based and collaborative filtering algorithms. The user's profile is updated implicitly and dynamically, based on the tracking of their reading. Beyond personalization, the ePaper can also provide the user with a standard edition of a selected newspaper, as well as browsing capabilities in a repository of news items. The layout of the newspaper is adapted to the specifications of the reading device and to the user's preferences. In this overview paper, we highlight the main research challenges involved in the development of ePaper and describe how we addressed them.

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Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

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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.

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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

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

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

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Abstract

Digital libraries have become one of the most important Web services for information seeking. One of their main drawbacks is their global approach: In general, there is just one interface for all users. One of the key elements in improving user satisfaction in digital libraries is personalization. When considering personalizing factors, cognitive styles have been proved to be one of the relevant parameters that affect information seeking. This justifies the introduction of cognitive style as one of the parameters of a Web personalized service. Nevertheless, this approach has one major drawback: Each user has to run a time-consuming test that determines his or her cognitive style. In this article, we present a study of how different classification systems can be used to automatically identify the cognitive style of a user using the set of interactions with a digital library. These classification systems can be used to automatically personalize, from a cognitive-style point of view, the interaction of the digital library and each of its users.

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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

This article discusses the rapidly emerging field of computer-based assessment for adaptive content in e-learning (National Research Council, 2001), which we call differentiated e-learning. In e-learning products, a variety of assessment approaches are being used for such diverse purposes as adaptive delivery of content, individualizing learning materials, dynamic feedback, cognitive diagnosis, score reporting, and course placement (Gifford, 2001). A recent paper at the General Teaching Council Conference in London, England, on teaching, learning, and accountability described assessment for personalized learning through e-learning products as a quiet revolution taking place in education (Hopkins, 2004). In our study, we examine approaches for the use of assessment evidence in e-learning in four case studies. The products in the case studies were selected for exhibiting at least one exemplary aspect regarding assessment and measurement. The principles of the Berkeley Evaluation & Assessment Research Center Assessment System (Wilson & Sloane, 2000) are used as a framework of analysis for these products with respect to key measurement principles.

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Abstract

Artificial intelligence (AI) is a multidisciplinary subject, typically studied as a research area within Computer Science. AI study aims at achieving a good understanding of the nature of intelligence and building intelligent agents which are computational systems demonstrating intelligent behavior. AI has been developed over more than 50 years. The topics studied in AI are quite broad, ranging from knowledge representation and reasoning, knowledge-based systems, machine learning and data mining, natural language processing, to search, image processing, robotics, and intelligent information systems. Numerous successful AI systems have been deployed in real-life applications in engineering, finance, science, health care, education, and service sectors. AI research has also significantly impacted the subject area of Library and Information Science (LIS), helping to develop smart Web search engines, personalized news filters, and knowledge-sharing and indexing systems. This entry briefly outlines the main topics studied in AI, samples some typical successful AI applications, and discusses the cross-fertilization between AI and LIS.