Knowledge graph (KG) plays an increasingly important role in recommender
systems. Recently, graph neural networks (GNNs) based model has gradually
become the theme of knowledge-aware recommendation (KGR). However, there is a
natural deficiency for GNN-based KGR models, that is, the sparse supervised
signal problem, which may make their actual performance drop to some extent.
Inspired by the recent success of contrastive learning in mining supervised
signals from data itself, in this paper, we focus on exploring the contrastive
learning in KG-aware recommendation and propose a novel multi-level cross-view
contrastive learning mechanism, named MCCLK. Different from traditional
contrastive learning methods which generate two graph views by uniform data
augmentation schemes such as corruption or dropping, we comprehensively
consider three different graph views for KG-aware recommendation, including
global-level structural view, local-level collaborative and semantic views.
Specifically, we consider the user-item graph as a collaborative view, the
item-entity graph as a semantic view, and the user-item-entity graph as a
structural view. MCCLK hence performs contrastive learning across three views
on both local and global levels, mining comprehensive graph feature and
structure information in a self-supervised manner. Besides, in semantic view, a
k-Nearest-Neighbor (kNN) item-item semantic graph construction module is
proposed, to capture the important item-item semantic relation which is usually
ignored by previous work. Extensive experiments conducted on three benchmark
datasets show the superior performance of our proposed method over the
state-of-the-arts. The implementations are available at:
this https URL

本文提出了一种新的多层交叉视角对比学习机制 MCCLK，包含三个图视角：全局结构视角、本地合作和语义视角，在语义视角中，还提出了一个 k 最近邻（kNN）项 - 项语义图构建模块，以捕捉通常被忽略的重要项 - 项语义关系，在三个基准数据集上进行了广泛的实验，结果表明了我们的方法优于现有技术。