Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems

2025-10-11

AI Summary

This work addresses two key challenges in recommender systems: insufficient interpretability and difficulty in modeling high-order semantic relationships. To this end, we propose an interpretable recommendation model that integrates knowledge graphs with a structure-aware attention mechanism. Methodologically, we design a structure-aware attention mechanism to dynamically weight multi-hop neighbors and construct hierarchical semantic paths, thereby capturing both implicit user-item preferences and high-order semantic associations. Furthermore, we jointly leverage graph neural networks and knowledge graph embedding within an end-to-end learning framework to achieve accurate and explainable recommendations. Experimental results on the Amazon Books dataset demonstrate that our model significantly outperforms state-of-the-art methods in Recall@20 and NDCG@20. It also exhibits superior convergence stability and inherent interpretability, validating that structure-aware modeling effectively enhances representation learning for complex user preferences.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• 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

Abstract

This paper designs and implements an explainable recommendation model that integrates knowledge graphs with structure-aware attention mechanisms. The model is built on graph neural networks and incorporates a multi-hop neighbor aggregation strategy. By integrating the structural information of knowledge graphs and dynamically assigning importance to different neighbors through an attention mechanism, the model enhances its ability to capture implicit preference relationships. In the proposed method, users and items are embedded into a unified graph structure. Multi-level semantic paths are constructed based on entities and relations in the knowledge graph to extract richer contextual information. During the rating prediction phase, recommendations are generated through the interaction between user and target item representations. The model is optimized using a binary cross-entropy loss function. Experiments conducted on the Amazon Books dataset validate the superior performance of the proposed model across various evaluation metrics. The model also shows good convergence and stability. These results further demonstrate the effectiveness and practicality of structure-aware attention mechanisms in knowledge graph-enhanced recommendation.

Problem

• Integrating knowledge graphs with attention mechanisms for explainable recommendations
• Enhancing implicit preference modeling through structure-aware neighbor aggregation
• Improving recommendation accuracy using multi-hop semantic path extraction

Innovation

• Integrates knowledge graphs with structure-aware attention mechanisms
• Uses graph neural networks with multi-hop neighbor aggregation
• Constructs multi-level semantic paths for contextual information extraction