A Framework for Filtering News and Managing Distributed Data

With the development and di usion of the Internet worldwide connection, a large amount of information is available to the users. Methods of information ltering and fetching are then required. This paper presents two approaches. The rst concerns the information ltering system ProFile based on an adaptation of the generalized probabilistic model of information retrieval. ProFile lters the netnews and uses a scale of 11 prede ned values of relevance. ProFile allows the user to update on{line the pro le 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 ltering data from distributed and heterogeneous archives and repositories. ABIS minimizes user's e ort in selecting the huge amount of available documents. The ltering engine memorizes both user preferences and past situations. ABIS compares documents with the past situations and nds the similarity scores on the basis of a memory-based reasoning approach.

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