. We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user

or organize search results differently for each user, based upon their interests, preferences, and information needs. There are many personalized web search algorithms for analyzing the user interests and producing the outcome quickly; User profiling, Hyperlink Analysis, Content Analysis and collaborative web

given text as positive, negative or Words prediction. neutral.

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback

In this paper, I present issues related to user profiles and their management in pervasive computing. I motivate the need for user profiles and present basic techniques that can be used in storing and managing profiles. I also present technigues that can be used to help profiling and Semantic Web

information on behalf of users or communities of users. In order to provide personalized assistance, personal agents rely on representations of user information interests and preferences contained in user profiles. In this paper, we present a summary of the state-of-the-art in user profiling in the context

in various papers that Information Scent can be used as a methodology for clustering a group of user profiles [Chi02], simulating a collection of users navigating thru the Web with an information need [Chi03], and providing navigational cues to users with transient information goals [Olston03]. We argue

A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant, personalized, and context-sensitive information delivery. Yet traditional approaches to user profiles rely on explicit, brittle models that go out of date very

solutions able to support users in finding documents according to their interests Learning user profiles in digital libraries 2 1 …and problems in e-commerce Critical aspect in e-commerce � Millions of products for sale � Customers overloaded with a large amount of product information � Searching may

A significant problem in many information filtering systems is the dependence on the user for the creation and maintenance of a user profile, which describes the user's interests. NewsWeeder is a netnews-filtering system that addresses this problem by letting the user rate his or her interest