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，其通过结构化知识丰富用户、物品及实体的表示，取得了显著性能提升。然而，其高昂的计算成本仍限制着研究者探索更复杂模型的能力。我们观察到训练效率的瓶颈源于知识图谱，这受到众所周知的"知识爆炸"问题困扰。近期已有研究尝试通过摘要技术压缩膨胀的知识图谱，但此类摘要节点可能忽略协同信号，且偏离知识图谱节点作为现实世界实体符号化抽象的本质。为此，本文提出一种面向推荐任务的知识图谱剪枝新方法KGTrimmer，在移除非必要节点的同时最小化性能损失。具体而言，我们设计了一个双视角重要性评估器：在集体视角中，我们基于丰富的协同信号提取社区共识，即若节点能吸引大量用户关注则被视为重要节点；在整体视角中，我们通过学习全局掩码，根据节点固有属性或整体流行度识别无价值节点。随后构建端到端的重要性感知图神经网络，通过注入过滤后的知识来增强有价值用户-物品协同信号的提取。最终生成具有轻量化、稳定性和鲁棒性的剪枝知识图谱，以提升后续推荐任务效率。我们在三个公开数据集上进行了大量实验，验证了KGTrimmer的有效性与泛化能力。