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At DART'09, held in conjunction with the 2009 IEEE/WIC/ACM
International Conference on Web Intelligence (WI 2009) and
Intelligent Agent Technology (IAT 2009) in Milan (Italy),
practitioners and researchers working on pervasive and intelligent
access to web services and distributed information retrieval met to
compare their work ad insights in such fascinating topics. Extended
and revised versions of their papers, together with selected and
invited original contributions, are collected in this book. Topics
covered are those that emerged at DART'09 as the most intriguing
and challenging: (i) community oriented tools and techniques as
infrastructure of the Web 2.0; (ii) agent technology applied to
virtual world scenarios; (iii) context aware information retrieval;
(iv) content based information retrieval; and (v) industrial
applications of information retrieval. Every chapter, before
discussing in depth the specific topic, presents a comprehensive
review of related work and state of the art, in the hope of this
volume to be of use in the years to come, to both researchers and
students.
Intelligent Information Access techniques attempt to overcome the
limi- tions of current search devices by providing personalized
information items and product/service recommendations. They
normally utilize direct or in- rect user input and facilitate the
information search and decision processes, according to user needs,
preferences and usage patterns. Recent devel- ments at the
intersection of Information Retrieval, Information Filtering,
MachineLearning,UserModelling,NaturalLanguageProcessingandHuman-
Computer Interaction o?er novel solutions that empower users to go
beyond
single-sessionlookuptasksandthataimatservingthemorecomplexrequi-
ment: "Tell me what I don't know that I need to know".
Information?ltering
systems,speci?callyrecommendersystems,havebeenrevolutionizingtheway
information seekers?nd what they want, becausethey e?ectively prune
large
informationspacesandhelpusersinselectingitemsthatbestmeettheirneeds
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
?ltering 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 part- ipants in the research event Intelligent Information
Access,heldinCagliari (Italy) in December 2008.
At DART'09, held in conjunction with the 2009 IEEE/WIC/ACM
International Conference on Web Intelligence (WI 2009) and
Intelligent Agent Technology (IAT 2009) in Milan (Italy),
practitioners and researchers working on pervasive and intelligent
access to web services and distributed information retrieval met to
compare their work ad insights in such fascinating topics. Extended
and revised versions of their papers, together with selected and
invited original contributions, are collected in this book. Topics
covered are those that emerged at DART'09 as the most intriguing
and challenging: (i) community oriented tools and techniques as
infrastructure of the Web 2.0; (ii) agent technology applied to
virtual world scenarios; (iii) context aware information retrieval;
(iv) content based information retrieval; and (v) industrial
applications of information retrieval. Every chapter, before
discussing in depth the specific topic, presents a comprehensive
review of related work and state of the art, in the hope of this
volume to be of use in the years to come, to both researchers and
students.
Intelligent Information Access techniques attempt to overcome the
limi- tions of current search devices by providing personalized
information items and product/service recommendations. They
normally utilize direct or in- rect user input and facilitate the
information search and decision processes, according to user needs,
preferences and usage patterns. Recent devel- ments at the
intersection of Information Retrieval, Information Filtering,
MachineLearning,UserModelling,NaturalLanguageProcessingandHuman-
Computer Interaction o?er novel solutions that empower users to go
beyond
single-sessionlookuptasksandthataimatservingthemorecomplexrequi-
ment: "Tell me what I don't know that I need to know".
Information?ltering
systems,speci?callyrecommendersystems,havebeenrevolutionizingtheway
information seekers?nd what they want, becausethey e?ectively prune
large
informationspacesandhelpusersinselectingitemsthatbestmeettheirneeds
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
?ltering 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 part- ipants in the research event Intelligent Information
Access,heldinCagliari (Italy) in December 2008.
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