  Due to the development of graph neural networks, graph-based representation
learning methods have made great progress in recommender systems. However, data
sparsity is still a challenging problem that most graph-based recommendation
methods are confronted with. Recent works try to address this problem by
utilizing side information. In this paper, we exploit the relevant and easily
accessible textual information by advanced natural language processing (NLP)
models and propose a light RGCN-based (RGCN, relational graph convolutional
network) collaborative filtering method on heterogeneous graphs. Specifically,
to incorporate rich textual knowledge, we utilize a pre-trained NLP model to
initialize the embeddings of text nodes. Afterward, by performing a simplified
RGCN-based node information propagation on the constructed heterogeneous graph,
the embeddings of users and items can be adjusted with textual knowledge, which
effectively alleviates the negative effects of data sparsity. Moreover, the
matching function used by most graph-based representation learning methods is
the inner product, which is not appropriate for the obtained embeddings that
contain complex semantics. We design a predictive network that combines
graph-based representation learning with neural matching function learning, and
demonstrate that this architecture can bring a significant performance
improvement. Extensive experiments are conducted on three publicly available
datasets, and the results verify the superior performance of our method over
several baselines.
