基于知识图谱的用户兴趣推荐  

User Interest Recommendation Based on Knowledge Graph

作　　者：沈学利 王嘉慧 SHEN Xue-Li;WANG Jia-Hui(Software College,Liaoning Technical University,Huludao 125105,China)

机构地区：[1]辽宁工程技术大学软件学院,葫芦岛125105

出　　处：《计算机系统应用》2025年第4期155-165,共11页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(42271409)。

摘　　要：在提供精准的用户兴趣推荐时,推荐系统的数据通常存在稀疏性问题,对于新上线的项目存在冷启动问题,缺乏用户交互数据,为解决上述问题,提出基于知识图谱的用户兴趣推荐算法.首先,在用户潜在兴趣中,通过多层图神经网络根据用户和项目的嵌入向量,获取用户和项目直接、间接和更深层次的关系,解决数据稀疏性问题.其次,在用户显式兴趣中,采用图结构增强根据评分权重随机删除用户和项目之间的显式关系,通过编码器分析新的用户和项目节点的关系,挖掘用户与项目间的交互关系,解决冷启动问题.最后,采用特征交叉压缩单元结合知识图谱嵌入与推荐任务实现特征共享,共享的特征更加深化项目与知识图谱实体间的互动,提高推荐的准确性.通过在Book-Crossing和Last.FM两个数据集上进行实验,结果证明与其他对比算法相比在AUC和ACC评价指标中有显著的提升.Data sparsity occurs in recommendation systems and the cold-start problem exists in newly launched items due to a lack of user interaction data when providing targeted user interest recommendations.To address these problems,this study proposes a user interest recommendation algorithm based on knowledge graphs.First,to tackle the data sparsity issue in users’potential interests,it employs a multi-layer graph neural network(GNN)to capture the direct,indirect,and deeper relationships between users and items through their embedding vectors.Second,for users’explicit interests,it introduces a graph structure enhancement technique to randomly delete explicit relationships between users and items based on rating weights.This method leverages an encoder to analyze the relationships of new users and item nodes,uncovering interactive relationships between users and items,thereby addressing the cold-start problem.Finally,a feature cross-compression module is used to combine knowledge graph embeddings with the recommendation task to achieve feature sharing.The shared features further deepen the interaction between items and knowledge graph entities,enhancing recommendation accuracy.Experiments conducted on the Book-Crossing and Last.FM datasets demonstrate that the proposed algorithm significantly outperforms other baseline algorithms in terms of AUC and ACC indicators.

关 键 词：推荐系统 知识图谱 图神经网络 特征交叉压缩 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP391.3[自动化与计算机技术—计算机科学与技术]