计算机与数字工程2025，Vol.53Issue(5)：1215-1221,7.DOI:10.3969/j.issn.1672-9722.2025.05.001
基于社交信息与知识图注意力网络的推荐算法
Recommendation Algorithm Based on Social Information and Knowledge Graph Attention Network
摘要
Abstract
In order to solve the problem that the recommendation algorithm based on knowledge graph ignores the correlation between users when using semantic information,resulting in the lack of neighborhood user information in the user's feature expres-sion,a recommendation algorithm(SKGAN)based on social information and knowledge graph attention network is proposed.Through knowledge embedding,the low-dimensional representation of the entity is obtained in the algorithm.The user features are obtained by performing convolution of the user-item bipartite graph,propagated and aggregated through social networks.An atten-tion mechanism is used to calculate the weights in the process of propagation.The enhanced user expression is combined with proj-ect representation of convolutional output of the item knowledge graph,so as to achieve recommendations.The experimental results show that the SKGAN algorithm outperforms the baseline model in both AUC and F1-Score metrics.关键词
推荐系统/社交网络/知识图谱/图卷积网络/注意力机制Key words
recommender system/social network/knowledge graph/graph convolutional network/attention mechanism分类
信息技术与安全科学引用本文复制引用
徐长林,王逊,黄树成..基于社交信息与知识图注意力网络的推荐算法[J].计算机与数字工程,2025,53(5):1215-1221,7.基金项目
国家自然科学基金项目(编号:61772244)资助. （编号:61772244）
