Giuliano Armano – författare

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.

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.