西安电子科技大学学报（自然科学版）2025，Vol.52Issue(5)：99-110,12.DOI:10.19665/j.issn1001-2400.20250603
通过知识感知的邻居过滤机制增强社交推荐
Enhancing social recommendation via the knowledge-aware neighbor filtering mechanism
摘要
Abstract
Social recommendation helps to improve the performance of personalized recommender systems by exploiting user social connections.However,most existing methods struggle to fully capture the complex relationships between users and items,while neglecting the issue of social inconsistency caused by irrelevant or even erroneous social ties,thus reducing the correctness of user embeddings and the accuracy of social recommendations.This paper proposes a knowledge-aware neighbor filtering mechanism for social recommendation(KFRec),aiming to resolve the aforementioned issues by integrating knowledge graphs with graph neural networks.First,this paper utilizes knowledge graph embedding techniques to vectorially represent users,items,and ratings,thereby capturing the latent relational patterns among them.Subsequently,these vectors are fed into a graph neural network to optimize the node representations of the graph neural network.To improve the model's consistency recognition capability,this paper dynamically constructs query vectors based on the user-item pairs to be evaluated,and comprehensively model the consistency scores between the query vectors and neighbor nodes using the knowledge graph.By sampling and aggregating more consistent neighbor nodes,the graph neural network model's ability to filter inconsistent neighbor nodes and node representations is enhanced.Extensive experiments on three public datasets demonstrate the superiority of our proposed KFRec over existing mainstream methods.关键词
推荐系统/图神经网络/社交推荐/邻居过滤Key words
recommender systems/graph neural network/social recommendation/neighbor filtering分类
信息技术与安全科学引用本文复制引用
周家亮,慕彩红,刘逸,陈云龙..通过知识感知的邻居过滤机制增强社交推荐[J].西安电子科技大学学报（自然科学版）,2025,52(5):99-110,12.基金项目
国家自然科学基金(62077038,61672405,62176196,62271374) （62077038,61672405,62176196,62271374）
