  In the information explosion era, recommender systems (RSs) are widely
studied and applied to discover user-preferred information. A RS performs
poorly when suffering from the cold-start issue, which can be alleviated if
incorporating Knowledge Graphs (KGs) as side information. However, most
existing works neglect the facts that node degrees in KGs are skewed and
massive amount of interactions in KGs are recommendation-irrelevant. To address
these problems, in this paper, we propose Differentiable Sampling on Knowledge
Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance
distribution of connected items from KGs and samples suitable items for
recommendation following this distribution. We devise a differentiable sampling
strategy, which enables the selection of relevant items to be jointly optimized
with the model training procedure. The experimental results demonstrate that
our model outperforms state-of-the-art KG-based recommender systems. The code
is available online at https://github.com/YuWang-1024/DSKReG.
