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Abstract : |
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 approaches while avoiding their shortcomings. Fab?s hybrid structure allows for automatic recognition of emergent issues relevant to various groups of users. It also enables two scaling problems, pertaining to the rising number of users and documents, to be addressed. COPYRIGHT 1997 Association for Computing Machinery Inc. By combining both collaborative and content-based filtering systems, Fab may eliminate many of the weaknesses found in each approach. Online readers are in need of tools to help them cope with the mass of content available on the World-Wide Web. In traditional media, readers are provided assistance in making selections. This includes both implicit assistance in the form of editorial oversight and explicit assistance in the form of recommendation services such as movie reviews and restaurant guides. The electronic medium offers new opportunities to create recommendation services, ones that adapt over time to track their evolving interests. Fab is such a recommendation system for the Web, and has been operational in, |
