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Intelligent Information Access [Paperback]

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  • Category: Books (Technology &Amp; Engineering)
  • ISBN-10:  3642263879
  • ISBN-10:  3642263879
  • ISBN-13:  9783642263873
  • ISBN-13:  9783642263873
  • Publisher:  Springer
  • Publisher:  Springer
  • Pages:  138
  • Pages:  138
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Feb-2012
  • Pub Date:  01-Feb-2012
  • SKU:  3642263879-11-SPRI
  • SKU:  3642263879-11-SPRI
  • Item ID: 100807060
  • List Price: $109.99
  • Seller: ShopSpell
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Written from a multidisciplinary perspective, Intelligent Information Access investigates new insights into methods, techniques and technologies for intelligent information access. The chapters are written by participants in the Intelligent Information Access meeting, held in Cagliari, Italy, in December 2008.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 ofcurrent search devices by providing personalized information items and product/service recommendations. They normally utilize direct or indirect user input andfacilitate the information search and decision processes, according to user needs,preferences and usage patterns. Recent developments at the intersection of InformationRetrieval, Information Filtering, Machine Learning, User Modelling, NaturalLanguage Processing and Human-Computer Interaction offer novel solutions thatempower users to go beyond single-session lookup tasks and that aim at servingthe more complex requirement: Tell me what I dont know that I need to know.Information filtering systems, specifically recommender systems, have been revolutionizingthe way information seekers find what they want, because they effectivelyprune large information spaces and help users in selecting items that best meet theirneeds and preferences. Recommender systems rely strongly on the use of variousmachine learning tools and algorithms for learning how to rank, or predict userevaluation, of items. Information Retrieval systems, on the other hand, also attemptto address similar filtering and ranking problems for pieces ol"

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