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Recommender systems attempt to predict the needs of Web users and provide them with recommendations to personalize their online experience. In this paper, we propose a neuro-fuzzy approach for the extraction of a recommendation model from usage data encoding user navigational behaviors. Such model is expressed as a set of fuzzy rules which may be exploited to provide personalized link suggestions to the users visiting a Web site. In particular, a neuro-fuzzy network is trained using information about user categories to discover a set of fuzzy rules capturing the associations between user behavior models and relevance degrees of pages to be recommended. A comparison with other recommendation approaches shows the effectiveness of the proposed neuro-fuzzy approach in finding good recommendation rules.