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The original publication is available at www.springerlink.comInternational audienceThe current Information Retrieval Systems return hundreds or thousands of documents in response to a query. Users consider only the first 20 or 30, but documents are often sorted according to the query and the results are perhaps not relevant to the users' needs. This situation is even more problematic when non expert users have difficulties in expressing their information requirements. One solution to this problem could be the use of a user model which would complement the query in order to find the best solutions for the user. Most personalized retrieval systems have a single user model for each user which works on the basis that this user will have always the same information needs. Our proposal however is a personalization method based on the current objective of the user. To this end, we have developed two probabilistic algorithms NBM and WNBM which support different kinds of multi parameter databases and in this paper we present new experiments with these algorithms in METIORE to validate our proposa

NBM and WNBM: Algorithms and Evaluation for Personalizing Information Retrieval in METIORE