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Fab is a recommendation system designed to help users sift through the enormous amount of information available in the World Wide Web. Operational since Dec. 1994, this system combines the content-based and collaborative methods of recommendation in a way that exploits the advantages of the two

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[Green and Bavelier, 2003] has grabbed national attention for suggesting that playing “action ” video and computer games has the positive effect of enhancing students ’ visual selective attention. But that finding is just one small part of a more important message that all parents and educators

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Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation, and knowledgebase development. We have developed a set of tools and services to support the process of achieving consensus on common shared ontologies by geographically

... a collaborative filtering system for Usenet news—a high-volume, high-turnover discussion list service on the Internet. Usenet newsgroups—the individual discussion lists—may carry hundreds of messages each day. While in theory the newsgroup organization allows readers to select the content

www.people.cornell.edu/pages/edo1/.

Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy

predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering