Knowledge Graph Driven Recommendation System Algorithm

TL;DR

This paper introduces KGLN, a graph neural network-based recommendation model that utilizes knowledge graph information to improve personalized recommendation accuracy, demonstrating superior performance over existing methods on benchmark datasets.

Contribution

The paper presents a novel GNN-based recommendation model that effectively integrates knowledge graph data and influence factors, enhancing recommendation performance.

Findings

KGLN outperforms benchmark methods in AUC on MovieLen-1M and Book-Crossing datasets.

Incorporating influence factors improves recommendation accuracy.

Multi-layer evolution enables access to extensive multi-order entity information.

Abstract

In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information. The final step involves integrating features of entities and users to produce a recommendation score. The model performance was evaluated by comparing its effects on various aggregation methods and influence factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%, respectively, which is better than existing benchmark methods like LibFM, DeepFM, Wide&Deep, and RippleNet.