The paper introduces a novel re-ranking approach for explainable recommender systems that leverages knowledge graphs to optimize explanations based on recency, popularity, and diversity, aiming to enhance explanation quality without compromising recommendation utility. Through experiments conducted on two public datasets, the study demonstrates that the proposed methods improve explanation quality while maintaining fairness across demographic groups. The authors identify three new properties that influence the perceived quality of explanations and design re-ranking strategies to optimize these properties. Additionally, the paper combines a literature review and user studies to thoroughly explore relevant explanation types, contributing by proposing new metrics for explanation quality, a suite of re-ranking approaches, and an evaluation of the impact on both recommendation utility and explanation metrics. Technologies used in the research include Python, scikit-learn, pandas, numpy, and networkx.