  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.
