The Graph Attention Recommendation Method for Enhancing User Features Based on Knowledge Graphs

Knowledge graphs have shown great potential in alleviating the data sparsity problem in recommendation systems. However, existing graph-attention-based recommendation methods primarily focus on user–item–entity interactions, overlooking potential relationships between users while introducing noisy entities and redundant high-order information. To address these challenges, this paper proposes a graph-attention-based recommendation method that enhances user features using knowledge graphs (KGAEUF). This method models user relationships through collaborative propagation, links entities via similar user entities, and filters highly relevant entities from both user–entity and user–relation perspectives to reduce noise interference. In multi-layer propagation, a distance-aware weight allocation mechanism is introduced to optimize high-order information aggregation. Experimental results demonstrate that KGAEUF outperforms existing methods on AUC and F1 metrics on the Last.FM and Book-Crossing datasets, validating the model’s effectiveness.