  In recommender systems, knowledge graph (KG) can offer critical information
that is lacking in the original user-item interaction graph (IG). Recent
process has explored this direction and shows that contrastive learning is a
promising way to integrate both. However, we observe that existing KG-enhanced
recommenders struggle in balancing between the two contrastive views of IG and
KG, making them sometimes even less effective than simply applying contrastive
learning on IG without using KG. In this paper, we propose a new contrastive
learning framework for KG-enhanced recommendation. Specifically, to make full
use of the knowledge, we construct two separate contrastive views for KG and
IG, and maximize their mutual information; to ease the contrastive learning on
the two views, we further fuse KG information into IG in a one-direction
manner.Extensive experimental results on three real-world datasets demonstrate
the effectiveness and efficiency of our method, compared to the
state-of-the-art. Our code is available through the anonymous
link:https://figshare.com/articles/conference_contribution/SimKGCL/22783382
