KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation. (arXiv:2205.12102v1 [cs.IR])

Leveraging graphs on recommender systems has gained popularity with the
development of graph representation learning (GRL). In particular, knowledge
graph embedding (KGE) and graph neural networks (GNNs) are representative GRL
approaches, which have achieved the state-of-the-art performance on several
recommendation tasks. Furthermore, combination of KGE and GNNs (KG-GNNs) has
been explored and found effective in many academic literatures. One of the main
characteristics of GNNs is their ability to retain structural properties among
neighbors in the resulting dense representation, which …

arxiv effects embedding graph knowledge knowledge graph recommendation