Recommendation and personalization attempt to reduce information overload and retain customers. While research in both recommender systems and personalization grew mainly out of information retrieval, both areas have emerged from nascent levels to veritable and challenging research areas in their own right. Whereas no technical or sophisticated methodologies exist by which to build such systems, the field also lacks a comprehensive, yet manageable survey by which to study recommenda-tion systems and personalization facilities. In this paper, we attempt to fill that gap by presenting a thematic approach toward studying recommendation and personalization. Specifically, we present three major representative personalization themes: rec-ommendation; induction, exploration, and exploitation of social networks; and personalization of information access. We unify the presentation of the three themes which we have extracted from the rich landscape of recommender system and personal-ization research via a functional metaphor, where inputs and output to a function are identified in each theme and instantiated through a number of systems and projects visited. In addition, we examine how a number of systems implement the function through various operators and techniques. Finally, we cover several broadening aspects, such as targeting, privacy and trust,

Information redundancy becomes a crucial problem in the Web when contents from different resources are automatically combined to produce a new WWW--publication.

The work presented in this paper takes a novel approach to the task of providing information to viewers of broadcast news. Instead of considering the broadcast news as the end product, this work uses it as a starting point to dynamically build an information space for the user to explore. This information space is designed to satisfy the user's information needs, by containing more breadth, depth, and points of view than the original broadcast story. The architecture and current implementation are discussed, and preliminary results from the analysis of some its components are presented.

WAIF is a new framework to facilitate easy user access for Internet users to relevant news items. WAIF supports new kinds of browsers, personalized filters, recommendation systems, and---most importantly---an evolution path intended to enable efficient deployment of new techniques that enhance the user retrieval experience.