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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...
In this paper we present NEWER, a neuro-fuzzy Web recommendation system that dynamically suggests interesting pages to the current user. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to extract a recommendation model in the form of fuzzy rules expressing associations between user categories and relevances of pages. The derived model is...
In this paper, we present REXWERE, a software tool designed and implemented in order to extract knowledge from Web usage data in the form of recommendation fuzzy rules useful to provide personalized link suggestions to the visitor of a Web site. REXWERE employs a hybrid approach that combines fuzzy reasoning and neural learning within a working scheme made of several steps. Firstly, a fuzzy clustering...
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