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

2025-01-07

AI Summary

To address weak interpretability and degraded performance in sparse scenarios—caused by coarse-grained user-item relationship modeling and implicit knowledge graph (KG) integration—this paper proposes an explicit relation-aware KG fusion framework. Methodologically, it introduces: (1) a novel dynamic projection vector-based mechanism for explicit entity-relation embedding fusion, enabling decoupled modeling of KG structure and semantic relations; (2) a relation-aware graph neural network coupled with an attention propagation module to enhance robust representation learning under sparse KG conditions; and (3) end-to-end traceable relation path generation for fine-grained interpretability. Extensive experiments on multiple benchmark datasets demonstrate significant improvements in Recall@20 and NDCG@20, validating the framework’s dual advantages in recommendation accuracy and model transparency.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Recommendation Systems
• Complex Relationship Modeling
• Data Sparsity

Innovation

• Knowledge Graph Attention Network
• Information Fusion
• Explainable Recommendation