  Recommendation systems, as widely implemented nowadays on various platforms,
recommend relevant items to users based on their preferences. The classical
methods which rely on user-item interaction matrices has limitations,
especially in scenarios where there is a lack of interaction data for new
items. Knowledge graph (KG)-based recommendation systems have emerged as a
promising solution. However, most KG-based methods adopt node embeddings, which
do not provide personalized recommendations for different users and cannot
generalize well to the new items. To address these limitations, we propose
Knowledge-enhanced User-Centric subgraph Network (KUCNet), a subgraph learning
approach with graph neural network (GNN) for effective recommendation. KUCNet
constructs a U-I subgraph for each user-item pair that captures both the
historical information of user-item interactions and the side information
provided in KG. An attention-based GNN is designed to encode the U-I subgraphs
for recommendation. Considering efficiency, the pruned user-centric computation
graph is further introduced such that multiple U-I subgraphs can be
simultaneously computed and that the size can be pruned by Personalized
PageRank. Our proposed method achieves accurate, efficient, and interpretable
recommendations especially for new items. Experimental results demonstrate the
superiority of KUCNet over state-of-the-art KG-based and collaborative
filtering (CF)-based methods.
