Abstract The existing knowledge graph-based recommendation models often lack a fine-grained consideration of collaborative information between users and items and overlook the high-order semantics and structural relationships within the graph paths. To address these issues, a dual-end recommendation algorithm integrating User Intent and Knowledge-Aware Graph Attention Networks (UIKGAN) is proposed. On the user end, the intent behind user-item interactions to refine the representation of collaborative information is modeled. By propagating relationship paths, UIKGAN aggregates deeper semantic and structural information from the knowledge graph to more accurately capture the extended representation of user intent and behavior patterns. On the item end, UIKGAN embeds and aggregates high-order neighboring triplet information using a knowledge- aware attention mechanism, enriching the feature representation of items. Additionally, this paper introduces an independence modeling module to optimize the loss function, providing better interpretability of user intent. Experiments were conducted on three public datasets, including comparative experiments with seven baseline models, ablation studies, hyperparameter sensitivity experiments, and sparse data issue analysis. The experimental results demonstrate that the UIKGAN model outperforms other baselines in overall performance, improving recommendation accuracy while effectively alleviating the issue of dataset sparsity.

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Teng Lv is a Professor, Ph.D., his research focuses on Recommendation Systems and Data Management. He is the Corresponding author of this paper. Yi Yu is a Master’s candidate, his Research Focuses on Recommendation Systems.