Knowledge Graph Driven Recommendation System Algorithm

• URL: http://arxiv.org/abs/2401.10244v3
• Date: Sat, 3 Feb 2024 17:14:46 GMT
• Title: Knowledge Graph Driven Recommendation System Algorithm
• Authors: Chaoyang Zhang, Yanan Li, Shen Chen, Siwei Fan, Wei Li
• Abstract summary: We propose a novel graph neural network-based recommendation model called KGLN. 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. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information.
• Score: 9.952420935326893
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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