000			03872nam a22004695i 4500
001			u374644
003			SIRSI
005			20160812084244.0
007			cr nn 008mamaa
008			100708s2010 gw | s |||| 0|eng d
020			_a9783642140006 _9978-3-642-14000-6
040			_cMX-MeUAM
050		4	_aQ342
082	0	4	_a006.3 _223
100	1		_aArmano, Giuliano. _eeditor.
245	1	0	_aIntelligent Information Access _h[recurso electrónico] / _cedited by Giuliano Armano, Marco Gemmis, Giovanni Semeraro, Eloisa Vargiu.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_a150p. 1 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v301
505	0		_aEnhancing 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.
520			_aIntelligent 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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aGemmis, Marco. _eeditor.
700	1		_aSemeraro, Giovanni. _eeditor.
700	1		_aVargiu, Eloisa. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642139994
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v301
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-14000-6
596			_a19
942			_cLIBRO_ELEC
999			_c202524 _d202524