In recent years there has been a wide-spread evolution of support tools that help users to accomplish a range of computer-mediated tasks. In this context, recommender systems have emerged as powerful user-support tools which provide assistance to users by facilitating access to relevant items. Nevertheless, recommender system technologies suffer from a number of limitations, mainly due to the lack of underlying elements for performing qualitative reasoning appropriately. Over the last few years, argumentation has been gaining increasing importance in several AI-related areas, mainly as a vehicle for facilitating rationally justifiable decision making when handling incomplete and potentially inconsistent information. In this setting, recommender systems can rely on argumentation techniques by providing reasoned guidelines or hints supported by a rationally justified procedure. This chapter presents a generic argument-based approach to characterize recommender system technologies, in which knowledge representation and inference are captured in terms of Defeasible Logic Programming, a general-purpose defeasible argumentation formalism based on logic programming. As a particular instance of our approach we analyze an argument-based search engine called ARGUENET, an application oriented towards providing recommendations on the web scenario.
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