The most highly-personalized recommender applications use persistent personalization to create recommendations that differ for
different customers, even when they are looking at the same items. These persistent recommenders employ user-to-user correlation,
attribute-based recommendation using persistent attribute preferences, or item-to-item correlation based on persistent item
preferences. They require customers to maintain persistent identities, but reward them with the greatest level of personal
recommendation. Examples of persistent personalization include My CDNOW, which uses user-to-user correlation, and
Amazon.com's Eyes and eBay's Personal Shopper, which use persistent attribute recommendation.