# An Efficient Recommendation Model Based on Knowledge Graph Attention-Assisted Network (KGATAX)

arXiv: https://arxiv.org/abs/2409.15315 · HTML: https://mlsanity.com/paper/2409.15315/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)

## Built on by (most cited first, 3 of 3)

- [Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues](https://mlsanity.com/paper/2411.06374/) (2024)
- [Robust Graph Neural Networks for Stability Analysis in Dynamic Networks](https://mlsanity.com/paper/2411.11848/) (2024)
- [Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models](https://mlsanity.com/paper/2412.18715/) (2024)

## Abstract (arXiv)

Recommendation systems play a crucial role in helping users filter through vast amounts of information. However, traditional recommendation algorithms often overlook the integration and utilization of multi-source information, limiting system performance. Therefore, this study proposes a novel recommendation model, Knowledge Graph Attention-assisted Network (KGAT-AX). We first incorporate the knowledge graph into the recommendation model, introducing an attention mechanism to explore higher order connectivity more explicitly. By using multilayer interactive information propagation, the model aggregates information to enhance its generalization ability. Furthermore, we integrate auxiliary information into entities through holographic embeddings, aggregating the information of adjacent entities for each entity by learning their inferential relationships. This allows for better utilization of auxiliary information associated with entities. We conducted experiments on real datasets to demonstrate the rationality and effectiveness of the KGAT-AX model. Through experimental analysis, we observed the effectiveness and potential of KGAT-AX compared to other baseline models on public datasets. KGAT-AX demonstrates better knowledge information capture and relationship learning capabilities.