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Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the ( CF ) 2 architecture, which uses local learning techniques to embed contextualdoi:10.1007/s10791-018-9332-3 pmid:30956536 pmcid:PMC6413629 fatcat:6o2fuvguzna2hkonp77tq5eivi