  Incorporating Knowledge Graphs (KG) into recommeder system has attracted
considerable attention. Recently, the technical trend of Knowledge-aware
Recommendation (KGR) is to develop end-to-end models based on graph neural
networks (GNNs). However, the extremely sparse user-item interactions
significantly degrade the performance of the GNN-based models, as: 1) the
sparse interaction, means inadequate supervision signals and limits the
supervised GNN-based models; 2) the combination of sparse interactions (CF
part) and redundant KG facts (KG part) results in an unbalanced information
utilization. Besides, the GNN paradigm aggregates local neighbors for node
representation learning, while ignoring the non-local KG facts and making the
knowledge extraction insufficient. Inspired by the recent success of
contrastive learning in mining supervised signals from data itself, in this
paper, we focus on exploring contrastive learning in KGR and propose a novel
multi-level interactive contrastive learning mechanism. Different from
traditional contrastive learning methods which contrast nodes of two generated
graph views, interactive contrastive mechanism conducts layer-wise
self-supervised learning by contrasting layers of different parts within
graphs, which is also an "interaction" action. Specifically, we first construct
local and non-local graphs for user/item in KG, exploring more KG facts for
KGR. Then an intra-graph level interactive contrastive learning is performed
within each graph, which contrasts layers of the CF and KG parts, for more
consistent information leveraging. Besides, an inter-graph level interactive
contrastive learning is performed between the local and non-local graphs, for
sufficiently and coherently extracting non-local KG signals. Extensive
experiments conducted on three benchmark datasets show the superior performance
of our proposed method over the state-of-the-arts.
