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A Novel Embedding Model for Relation Prediction in Recommendation Systems
2017
IEICE transactions on information and systems
It inevitably comes out information overload problem with the increasing available data on e-commence websites. Most existing approaches have been proposed to recommend the users personal significant and interesting items on e-commence websites, by estimating unknown rating which the user may rate the unrated item, i.e., rating prediction. However, the existing approaches are unable to perform user prediction and item prediction, since they just treat the ratings as real numbers and learn
doi:10.1587/transinf.2016edp7421
fatcat:7r3n4e7btrajvflqnsr7rubpoi