Recommender systems (RSs) are designed to provide personalized recommendations to users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve recommendation accuracy. In this study, however, we demonstrate that RSs do not necessarily perform worse even if the KG is downgraded to the user-item interaction graph only (or removed). We propose an evaluation framework KG4RecEval to systematically evaluate how much a KG contributes to the recommendation accuracy of a KG-based RS, using our defined metric KGER (KG utilization efficiency in recommendation). We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users. Our extensive experiments on four commonly used datasets and a number of state-of-the-art KG-based RSs reveal that: to remove, randomly distort or decrease knowledge does not necessarily decrease recommendation accuracy, even for cold-start users. These findings inspire us to rethink how to better utilize knowledge from existing KGs, whereby we discuss and provide insights into what characteristics of datasets and KG-based RSs may help improve KG utilization efficiency.

翻译：推荐系统（RS）旨在为用户提供个性化推荐。近年来，知识图谱（KG）被广泛应用于推荐系统中以提高推荐准确性。然而，本研究表明，即使将知识图谱降级为仅包含用户-物品交互图（甚至移除），推荐系统的性能也不一定更差。我们提出评估框架KG4RecEval，利用定义指标KGER（知识图谱在推荐中的利用效率），系统评估知识图谱对基于KG的推荐系统准确性的贡献程度。我们考虑了知识图谱中知识被完全移除、随机扭曲和减少的场景，以及面向冷启动用户的推荐场景。在四个常用数据集和多个最先进的基于KG的推荐系统上进行的广泛实验揭示：移除、随机扭曲或减少知识并不一定会降低推荐准确性，即使对冷启动用户也是如此。这些发现促使我们重新思考如何更好地利用现有知识图谱中的知识，并据此讨论和提出关于数据集和基于KG的推荐系统的哪些特征可能有助于提高知识图谱利用效率的见解。