Intelligent Information Access / Studies in Computational Intelligence Bd.301 (PDF)

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.