Springer Intelligent Information Access: 301 (Studies in Computational Intelligence, 301)

Product Description 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. From the Back Cover 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.