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In the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by providing them with information and/or services that they need without explicitly asking for them. In this paper we propose NEWER, a usage-based Web...
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 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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