Knowledge-Enhanced Recommendation with User-Centric Subgraph Network

arXiv:2403.14377v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.ai cs.ir cs.lg data graph knowledge knowledge graph limitations network platforms recommendation recommendation systems systems type