南京大学学报（自然科学版）2026，Vol.62Issue(2)：258-266,9.DOI:10.13232/j.cnki.jnju.2026.02.008
基于个人知识图谱表示学习的推荐算法
Recommendation algorithms based on personalized knowledge graph representation learning
摘要
Abstract
With the rapid development of Internet technology,recommendation systems are playing an increasingly important role in addressing information overload.However,traditional recommendation methods often overlook the complex latent relationships between users' personalized features and items,leading to suboptimal performance.To tackle this issue,we propose PKGRec,a Feature-Interactive Graph Neural Network recommendation model based on Personal Knowledge Graphs.PKGRec integrates users' personal knowledge graphs with public knowledge graphs and captures complex interaction patterns among entities through a feature-entity interaction layer.Furthermore,we design a preference-aware attention mechanism that enables fine-grained user representation learning based on the user's interaction weights with different items,effectively enhancing the model's expressive power.We evaluate our model on two large-scale real-world datasets:NetEase Cloud Music and KuaiRec.Experimental results show that PKGRec significantly outperforms eight strong baselines,including BPRMF,NFM,and CKE,across three evaluation metrics:Precision,Recall,and NDCG.Notably,PKGRec exhibits significant advantages in cold-start and long-tail recommendation scenarios,validating the effectiveness of personal knowledge graphs in enhancing recommendation systems.关键词
个人知识图谱/推荐系统/图神经网络/特征交互/注意力机制Key words
personalized knowledge graph/recommendation system/graph neural network/graph neural network/attention mechanism分类
信息技术与安全科学引用本文复制引用
王晨旭,沈彦成,胡骏,王世豪..基于个人知识图谱表示学习的推荐算法[J].南京大学学报（自然科学版）,2026,62(2):258-266,9.基金项目
国家自然科学基金(62272379,T2341003),陕西省自然科学基础研究计划(2025JC-JCQN-081),中央高校基本科研业务费专项资金(xzy012023068),西安交通大学人工智能研究基金(2025YXYC004) （62272379,T2341003）
