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Traditional evaluation metrics based on statistical formulas employed to assess the performance of a recommender system are now considered to be inadequate when utilized solely. New metrics considering the quality of user-system interaction alongside with traditional ones have been proposed in evaluation process in order to arrive at more adequate results. Generating explanations for recommendations is a research topic that has emerged as a way to evaluate the system with respect to various criteria considering users' opinions and feelings. This paper presents the state-of-the-art with respect to the explanation of a recommendation.
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