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

2025/10/11 by Shaocheng Lyu, Lyu, Shuangquan, Ming Wang +8 · 5 citations
Computer Science · #Advanced Graph Neural Networks #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques

paper · pdf · doi:10.48550/arxiv.2510.10109

openalex publication_date 2025/10/11 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28

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.

Citations

• Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models
• Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs
• Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models
• Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks
• High Fidelity Text to Image Generation with Contrastive Alignment and Structural Guidance
• SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity
• CSRec: Rethinking Sequential Recommendation from A Causal Perspective
• DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems
• A Survey on the Fairness of Recommender Systems
• Self-Supervised Learning for Recommender Systems: A Survey
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
• Bias and Debias in Recommender System: A Survey and Future Directions
• Bias and Debias in Recommender System: A Survey and Future Directions

Cited by

• Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Making
• Multi-Scale Feature Fusion and Graph Neural Network Integration for Text Classification with Large Language Models
• Graph Neural Networks for User Satisfaction Classification in Human-Computer Interaction
• Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Models
• Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning

Related

• An Efficient Recommendation Model Based on Knowledge Graph Attention-Assisted Network (KGATAX)
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
• Deep Learning on Knowledge Graph for Recommender System: A Survey
• Knowledge Graph Driven Recommendation System Algorithm
• Explainable Reasoning over Knowledge Graphs for Recommendation