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

arXiv: https://arxiv.org/abs/2510.10109 · HTML: https://mlsanity.com/paper/2510.10109/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03279/) (2025)
- [Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Models](https://mlsanity.com/paper/2511.03981/) (2025)
- [Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Making](https://mlsanity.com/paper/2512.09440/) (2025)
- [Multi-Scale Feature Fusion and Graph Neural Network Integration for Text Classification with Large Language Models](https://mlsanity.com/paper/2511.05752/) (2025)

## Abstract (arXiv)

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.