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.

我们提出了一种名为 KGTrimmer 的新颖方法，用于为推荐任务修剪知识图，以去除不必要的节点并最小化性能下降，通过从群体视图和整体视图的角度设计重要性评估器和端到端的有重要性的图神经网络，生成轻量、稳定和健壮的修剪后的知识图。