ROBIN BURKE nformation in a user model can vary greatly. Rich's system used hand-crafted attributes with numeric confidence values. Pazzani's model uses Winnow to extract features from users' home pages that are predictive of liking certain restaurants Demographic techniques form people-to-people' correlations like collaborative ones, but use different data. The benefit of a demographic approach is that it may not require a history of user ratings of the type needed by collaborative and content-based techniques Content-based recommendation is an outgrowth and continuation of information filtering research(Belkin Croft, 1992). In a content-based system, the objects of interest are defined by their associated features. For example, text recommendation systems like the newsgroup filtering system News Weeder (Lang, 1995)uses the words of their texts as features. A content-based recommender learns a profile the user's interests based on the features present in objects the user has rated Schafer, Konstan and Riedl call this item-to-item correlation. The type of user profile derived by a content-based recommender depends on the learning method employed. Decision trees, neural nets, and vector-based representations have all been used. As in the collaborative case, content-based user profiles are long-term models and updated as more evidence about user preferences is observed Utility-based and knowledge-based recommenders do not attempt to build long-term generalizations about their users, but rather base their advice on an evalu ation of the match between a user's need and the set of options available Utility-based recommenders make suggestions based on a computation of the utility of each object for the user. Of course, the central problem is how to create a utility function for each user. Tete-a-Tete and the e-commerce site Persona logic each have different techniques for arriving at a user-specific utility function and applying it to the objects under consideration( Guttman, 1998). The user profile therefore is the utility function that the system has derived for the user, and the system employs onstraint satisfaction techniques to locate the best match. The benefit of utility-based recommendation is that it can factor non-product attributes, such as vendor reliability and product availability, into the utility computation, making it possible for example to trade off price against delivery schedule for a user who has an immediate need Knowledge-based recommendation attempts to suggest objects based on inferences about a user's needs and preferences. In some sense, all recommendation techniques could be described as doing some kind of inference. Knowledge-based approaches are distinguished in that they have functional knowledge: they have knowledge abor how a particular item meets a particular user need, and can therefore reason about the relationship between a need and a possible recommendation. The user profil can be any knowledge structure that supports this inference. In the simplest case, in Google, it may simply be the query that the user has formulated. In others, Schoorexampleseethecollegeguidesavailableathttp://www.peronalogic.aolcom/go/grad- Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission