Probabilistic learning for selective dissemination of information

New methods and new systems are needed to filter or to selectively distribu te the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact i nformation that fulfills user's interests with the minimum effort by the us er to describe it. Such a system will have to be adaptive to the user chang ing interest, In this paper we describe and evaluate a learning model for i nformation filtering which is an adaptation of the generalized probabilisti c model of Information Retrieval. The model is based on the concept of 'unc ertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile. (C) 1999 Elsev ier Science Ltd. All rights reserved.