  Incorporating knowledge graph as side information has become a new trend in
recommendation systems. Recent studies regard items as entities of a knowledge
graph and leverage graph neural networks to assist item encoding, yet by
considering each relation type individually. However, relation types are often
too many and sometimes one relation type involves too few entities. We argue
that it is not efficient nor effective to use every relation type for item
encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational
Knowledge Graphs for Recommendation), which explicitly distinguish the
influence of different relations for item representation learning. We first
construct virtual relational graphs (VRKGs) by an unsupervised learning scheme.
We also design a local weighted smoothing (LWS) mechanism for encoding nodes,
which iteratively updates a node embedding only depending on the embedding of
its own and its neighbors, but involve no additional training parameters. We
also employ the LWS mechanism on a user-item bipartite graph for user
representation learning, which utilizes encodings of items with relational
knowledge to help training representations of users. Experiment results on two
public datasets validate that our VRKG4Rec model outperforms the
state-of-the-art methods. The implementations are available at
https://github.com/lulu0913/VRKG4Rec.

Information Retrieval;Artificial Intelligence