融合知识图谱与图注意力的电子商务推荐算法

E-Commerce Recommendation Algorithm Integrating Knowledge Graph and Attention

摘要: 随着大数据时代的发展，电子商务进入了新的发展阶段，推荐系统已成为提升用户体验和促进销售的关键技术。本文提出一种融合知识图谱(KG)与图注意力(GAT)的电子商务推荐算法(E-KGAT)。首先，将用户行为数据与商品属性相结合构建协同知识图谱并进行嵌入表示。然后，设计了一个基于注意力机制的嵌入传播层，捕获节点间的相互作用与依赖关系。此外，通过层聚合机制整合各层的节点表示，预测用户对商品的匹配得分，从而完成推荐任务。最后，采用BCE损失函数和Adam优化器优化模型性能，确保模型在训练过程中准确学习用户和商品的嵌入表示。实验结果表明，通过与其他算法结果比较和消融实验结果比较，证明本算法相比典型的推荐算法不仅能更深入理解用户偏好，还能准确捕获商品之间的关系，从而实现更高的准确性和可解释性。

Abstract: With the development of the big data era, e-commerce has entered a new stage of development, and recommender system has become a key technology to enhance user experience and promote sales. This paper proposes an e-commerce recommendation algorithm (E-KGAT) that integrates knowledge graph (KG) and graph attention (GAT). First, user behavior data and product attributes are combined to construct a collaborative knowledge graph and embedded representation. Then, an embedding propagation layer based on the attention mechanism is designed to capture the interactions and dependencies among nodes. In addition, the node representations of each layer are integrated through the layer aggregation mechanism to predict the user’s matching score for commodities, thus accomplishing the recommendation task. Finally, the binary cross-entropy loss function and Adam optimizer are used to optimize the model performance and ensure that the model accurately learns the embedded representations of users and commodities during training. The experimental results show that by comparing with other algorithms and comparing with the results of ablation experiments, it is proved that the present algorithm not only understands the user preferences more deeply than typical recommendation algorithms, but also accurately captures the complex relationship between commodities, thus realizing higher accuracy and interpretability.