A model and method of generating context-driven recommendations for recommendation systems with multi-criteria ratings are proposed, applicable when the user's attitude to the object is fixed not by using one integral criterion (assessment, overall rating), but by using a set of individual criteria that evaluate different aspects of the object. The proposed model and method allow to solve two main problems of using recommender systems: to rank objects according to the predicted subjective integral utility with given weights of partial criteria and to rank objects according to the predicted subjective integral utility in a given context.
recommendation systems, recommender systems, multi-criteria optimization, weighted sum method, collaborative filtering, content filtering, context-driven systems.
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