An Adaptive Web Page Recommendation Service

An adaptive recommendation service seeks to adapt to its users, providing increasingly personalized rec-ommendations over time. In this paper we introduce the \Fab " adaptive web page recommendation ser-vice. There has been much research on analyzing doc-ument content in order to improve recommendations or search results. More recently researchers have be-gun to explore how the similarities between users can be exploited to the same ends. The Fab system strikes a balance between these two approaches, taking ad-vantage of the shared interests among users without losing the benets of the representations provided by content analysis. Running since March 1996, it has been populated with a collection of agents for the col-lection and selection of web pages, whose interaction fosters emergent collaborative properties. In this pa-per we explain the design of the system architecture and report the results of our rst experiment, eval-uating recommendations provided to a group of test users.