  Knowledge graphs (KGs) have proven to be effective for high-quality
recommendation, where the connectivities between users and items provide rich
and complementary information to user-item interactions. Most existing methods,
however, are insufficient to exploit the KGs for capturing user preferences, as
they either represent the user-item connectivities via paths with limited
expressiveness or implicitly model them by propagating information over the
entire KG with inevitable noise. In this paper, we design a novel hierarchical
attentive knowledge graph embedding (HAKG) framework to exploit the KGs for
effective recommendation. Specifically, HAKG first extracts the expressive
subgraphs that link user-item pairs to characterize their connectivities, which
accommodate both the semantics and topology of KGs. The subgraphs are then
encoded via a hierarchical attentive subgraph encoding to generate effective
subgraph embeddings for enhanced user preference prediction. Extensive
experiments show the superiority of HAKG against state-of-the-art
recommendation methods, as well as its potential in alleviating the data
sparsity issue.
