Intelligent Information Access [recurso electrónico] / edited by Giuliano Armano, Marco Gemmis, Giovanni Semeraro, Eloisa Vargiu.

Por: Armano, Giuliano [editor.]Colaborador(es): Gemmis, Marco [editor.] | Semeraro, Giovanni [editor.] | Vargiu, Eloisa [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Studies in Computational Intelligence ; 301Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Descripción: 150p. 1 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642140006Tema(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónicoTexto
Contenidos:
Enhancing Conversational Access to Information through a Socially Intelligent Agent -- Annotating and Identifying Emotions in Text -- Improving Ranking by Respecting the Multidimensionality and Uncertainty of User Preferences -- Data Mining on Folksonomies -- A Uniform Theoretic Approach to Opinion and Information Retrieval -- A Suite of Semantic Web Tools Supporting Development of Multilingual Ontologies.
En: Springer eBooksResumen: Intelligent Information Access techniques attempt to overcome the limitations of current search devices by providing personalized information items and product/ service recommendations. They normally utilize direct or indirect user input and facilitate the information search and decision processes, according to user needs, preferences and usage patterns. Recent developments at the intersection of Information Retrieval, Information Filtering, Machine Learning, User Modelling, Natural Language Processing and Human-Computer Interaction offer novel solutions that empower users to go beyond single-session lookup tasks and that aim at serving the more complex requirement: “Tell me what I don’t know that I need to know”. Information filtering systems, specifically recommender systems, have been revolutionizing the way information seekers find what they want, because they effectively prune large information spaces and help users in selecting items that best meet their needs and preferences. Recommender systems rely strongly on the use of various machine learning tools and algorithms for learning how to rank, or predict user evaluation, of items. Information Retrieval systems, on the other hand, also attempt to address similar filtering and ranking problems for pieces of information such as links, pages, and documents. But they generally focus on the development of global retrieval techniques, often neglecting individual user needs and preferences. The book aims to investigate current developments and new insights into methods, techniques and technologies for intelligent information access from a multidisciplinary perspective. It comprises six chapters authored by participants in the research event Intelligent Information Access, held in Cagliari (Italy) in December 2008.
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos Q342 (Browse shelf(Abre debajo)) 1 No para préstamo 374644-2001

Enhancing Conversational Access to Information through a Socially Intelligent Agent -- Annotating and Identifying Emotions in Text -- Improving Ranking by Respecting the Multidimensionality and Uncertainty of User Preferences -- Data Mining on Folksonomies -- A Uniform Theoretic Approach to Opinion and Information Retrieval -- A Suite of Semantic Web Tools Supporting Development of Multilingual Ontologies.

Intelligent Information Access techniques attempt to overcome the limitations of current search devices by providing personalized information items and product/ service recommendations. They normally utilize direct or indirect user input and facilitate the information search and decision processes, according to user needs, preferences and usage patterns. Recent developments at the intersection of Information Retrieval, Information Filtering, Machine Learning, User Modelling, Natural Language Processing and Human-Computer Interaction offer novel solutions that empower users to go beyond single-session lookup tasks and that aim at serving the more complex requirement: “Tell me what I don’t know that I need to know”. Information filtering systems, specifically recommender systems, have been revolutionizing the way information seekers find what they want, because they effectively prune large information spaces and help users in selecting items that best meet their needs and preferences. Recommender systems rely strongly on the use of various machine learning tools and algorithms for learning how to rank, or predict user evaluation, of items. Information Retrieval systems, on the other hand, also attempt to address similar filtering and ranking problems for pieces of information such as links, pages, and documents. But they generally focus on the development of global retrieval techniques, often neglecting individual user needs and preferences. The book aims to investigate current developments and new insights into methods, techniques and technologies for intelligent information access from a multidisciplinary perspective. It comprises six chapters authored by participants in the research event Intelligent Information Access, held in Cagliari (Italy) in December 2008.

19

Con tecnología Koha