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Explanation of spreading activation based recommendations
In this paper, we introduce an approach for explaining recommendations in environments that are based on semantic models. Using a constrained Spreading Activation (CSA) technique for recommendation generation, we store and exploit the activation paths leading to recommendations. These paths later can be used to generate both verbal explanations and relevance feedback forms.
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