基于个人知识图谱表示学习的推荐算法OA

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.