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A key feature in developing an effective web personalization system is to build and model dynamic user profiles. In this paper, we propose a multi-agent approach for building a dynamic user profile that is effectively capable of learning and adapting to user behaviour. The main goal is to implicitly track user browsing behaviour in order to extract short-term and long-term user interests. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology. In this paper, we focus on the learning and the adaptation processes that are essential in modelling a dynamic user profile. Our proposed model has been integrated with a personalized search system and experiments show that our system is able to effectively model a dynamic user profile that is capable of learning and adapting to user behaviour. Experiments also show that our model achieved a higher performance than non-personalized system.