Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation

TL;DR

This paper introduces HAKG, a hierarchical attentive framework that extracts and encodes subgraphs from knowledge graphs to improve personalized recommendation accuracy and address data sparsity.

Contribution

The paper proposes a novel hierarchical attentive subgraph encoding method for knowledge graph-based recommendation, enhancing expressiveness and noise reduction.

Findings

HAKG outperforms state-of-the-art recommendation methods.

It effectively alleviates data sparsity issues.

The subgraph extraction captures rich semantic and topological information.

Abstract

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.