  Existing explainable recommender systems have mainly modeled relationships
between recommended and already experienced products, and shaped explanation
types accordingly (e.g., movie "x" starred by actress "y" recommended to a user
because that user watched other movies with "y" as an actress). However, none
of these systems has investigated the extent to which properties of a single
explanation (e.g., the recency of interaction with that actress) and of a group
of explanations for a recommended list (e.g., the diversity of the explanation
types) can influence the perceived explaination quality. In this paper, we
conceptualized three novel properties that model the quality of the
explanations (linking interaction recency, shared entity popularity, and
explanation type diversity) and proposed re-ranking approaches able to optimize
for these properties. Experiments on two public data sets showed that our
approaches can increase explanation quality according to the proposed
properties, fairly across demographic groups, while preserving recommendation
utility. The source code and data are available at
https://github.com/giacoballoccu/explanation-quality-recsys.
