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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.doi:10.1109/icect.2004.1319762 dblp:conf/wecwis/RigouST04 fatcat:ruylsutnrzc4fofpjwzkaakr5q