KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

TL;DR

KGIF introduces a novel framework that explicitly fuses entity and relation embeddings using self-attention, improving recommendation accuracy, robustness, and interpretability in knowledge graph-based systems.

Contribution

It presents a new method for explicit information fusion in knowledge graphs, enhancing recommendation quality and explainability compared to prior implicit approaches.

Findings

Enhanced recommendation performance on benchmark datasets

Improved robustness in sparse knowledge graph scenarios

Generated explainable recommendations through interpretable path visualization

Abstract

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances underutilized. This study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization.