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Existing explainable recommender systems have mainly modeled relationshipsbetween recommended and already experienced products, and shaped explanationtypes accordingly (e.g., movie "x" starred by actress "y" recommended to a userbecause that user watched other movies with "y" as an actress). However, noneof these systems has investigated the extent to which properties of a singleexplanation (e.g., the recency of interaction with that actress) and of a groupof explanations for a recommended list (e.g., the diversity of the explanationtypes) can influence the perceived explaination quality. In this paper, weconceptualized three novel properties that model the quality of theexplanations (linking interaction recency, shared entity popularity, andexplanation type diversity) and proposed re-ranking approaches able to optimizefor these properties. Experiments on two public data sets showed that ourapproaches can increase explanation quality according to the proposedproperties, fairly across demographic groups, while preserving recommendationutility. The source code and data are available athttps://github.com/giacoballoccu/explanation-quality-recsys.

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations