Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement. Nevertheless, its unaffordable computational cost still limits researchers from exploring more sophisticated models. We observe that the bottleneck for training efficiency arises from the knowledge graph, which is plagued by the well-known issue of knowledge explosion. Recently, some works have attempted to slim the inflated KG via summarization techniques. However, these summarized nodes may ignore the collaborative signals and deviate from the facts that nodes in knowledge graph represent symbolic abstractions of entities from the real-world. To this end, in this paper, we propose a novel approach called KGTrimmer for knowledge graph pruning tailored for recommendation, to remove the unessential nodes while minimizing performance degradation. Specifically, we design an importance evaluator from a dual-view perspective. For the collective view, we embrace the idea of collective intelligence by extracting community consensus based on abundant collaborative signals, i.e. nodes are considered important if they attract attention of numerous users. For the holistic view, we learn a global mask to identify the valueless nodes from their inherent properties or overall popularity. Next, we build an end-to-end importance-aware graph neural network, which injects filtered knowledge to enhance the distillation of valuable user-item collaborative signals. Ultimately, we generate a pruned knowledge graph with lightweight, stable, and robust properties to facilitate the following-up recommendation task. Extensive experiments are conducted on three publicly available datasets to prove the effectiveness and generalization ability of KGTrimmer.

翻译：近年来，基于知识图谱的推荐系统（KGRS）蓬勃发展，通过结构化知识丰富用户、物品和实体的表示，取得了显著改进。然而，其高昂的计算成本仍限制了研究人员探索更复杂模型的可能性。我们观察到，训练效率的瓶颈源于知识图谱，而知识图谱正遭受广为人知的知识爆炸问题。近期，部分工作尝试通过摘要技术来优化膨胀的知识图谱，但这些摘要节点可能忽略协同信号，并偏离知识图谱中节点代表现实世界实体符号化抽象这一事实。为此，本文提出一种专为推荐定制的知识图谱剪枝方法——KGTrimmer，在最小化性能损失的同时去除不必要节点。具体而言，我们从双重视角设计重要性评估器：在集体视角下，我们利用协同信号提取社区共识，即若节点吸引大量用户关注则被视为重要；在整体视角下，我们学习全局掩码，通过节点的固有属性或整体流行度识别无价值节点。随后，我们构建端到端的重要性感知图神经网络，注入过滤后的知识以增强有效用户-物品协同信号的提炼。最终，我们生成轻量、稳定且鲁棒的精简知识图谱，以促进后续推荐任务。在三个公开数据集上的大量实验证明了KGTrimmer的有效性和泛化能力。