A Framework for Filtering News and Managing Distributed Data

With the development and diffusion of the Internet worldwide connection, a large amount of information is available to the users. Methods of information filtering and fetching are then required. This paper presents two approaches. The first concerns the information filtering system ProFile based on an adaptation of the generalized probabilistic model of information retrieval. ProFile filters the netnews and uses a scale of 11 predefined values of relevance. ProFile allows the user to update on--line the profile and to check the discrepancy between the assessment and the prediction of relevance of the system. The second concerns ABIS, an intelligent agent for supporting users in filtering data from distributed and heterogeneous archives and repositories. ABIS minimizes user s effort in selecting the huge amount of available documents. The filtering engine memorizes both user preferences and past situations. ABIS compares documents with the past situations and finds the similarity scores on the basis of a memory-based reasoning approach