Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths between recommended products and already experienced products and, then, turn such paths into textual explanations for the user. Unfortunately, evaluation protocols in this field appear heterogeneous and limited, making it hard to contextualize the impact of the existing methods. In this paper, we replicated three state-of-the-art relevant path reasoning recommendation methods proposed in top-tier conferences. Under a common evaluation protocol, based on two public data sets and in comparison with other knowledge-aware methods, we then studied the extent to which they meet recommendation utility and beyond objectives, explanation quality, and consumer and provider fairness. Our study provides a picture of the progress in this field, highlighting open issues and future directions. Source code: \url{https://github.com/giacoballoccu/rep-path-reasoning-recsys}.

翻译：路径推理是一种值得注意的建议方法,它基于知识图(KG),模拟高顺序用户-产品关系。这种方法可以在建议的产品和已经有经验的产品之间找到推理路径,然后将这种路径转化为用户的文字解释。不幸的是,这一领域的评价协议似乎多种多样,而且有限,难以根据背景说明现有方法的影响。在本文中,我们复制了在最高级会议上提出的三种最先进的相关路径推理建议方法。在一项共同的评价协议下,基于两个公共数据集,并与其他知识认知方法进行比较,我们随后研究了它们在多大程度上满足了建议效用,超越了目标、解释质量以及消费者和供应商的公平性。我们的研究介绍了该领域的进展,突出了开放的问题和未来方向。源代码:\url{https://github.com/gicoballoccu/repath-caling-recsysy}。