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In this paper we present an algorithm for efficient personalized clustering. The algorithm combines the orthogonal range search with the k-windows algorithm. It offers a real-time solution for the delivery of personalized services in online shopping environments, since it allows on-line consumers to model their preferences along multiple dimensions, search for product information, and then use the clustered list of products and services retrieved for making their purchase decisions.
Date of Conference: 6-9 July 2004