Probabilistic learning for selective dissemination of information

New methods and new systems are needed to ®lter or to selectively distribute the increasing volume of electronic information being produced nowadays. An e€ective information ®ltering system is one that provides the exact information that ful®lls user's interests with the minimum e€ort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information ®ltering which is an adaptation of the generalized probabilistic model of Information Retrieval. The model is based on the concept of `uncertainty 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 ®ltering system called ProFile. # 1999 Elsevier Science Ltd. All rights reserved.