基于知识图谱的稀疏数据协同过滤推荐算法  

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph for Sparse Data

作　　者：许雪晶 林辰玮 XU Xuejing;LIN Chenwei(School of Information Engineering,Putian University,Putian 351100,Fujian,China;Meizhouwan Vocational Technology College,Putian 351119,Fujian,China)

机构地区：[1]莆田学院信息工程学院,福建莆田351100 [2]湄洲湾职业技术学院,福建莆田351119

出　　处：《科技和产业》2025年第6期30-35,共6页Science Technology and Industry

基　　金：福建省中青年教师教育科研项目(科技类)(JAT210399,JAT241369)。

摘　　要：因缺乏足够的交互关系支撑导致推荐精度不佳,对此,提出基于知识图谱的稀疏数据协同过滤推荐算法。抽取用户与物品的交互关系,构建知识图谱,利用知识图谱中的实体关系对用户和物品进行扩展表示。结合卷积神经网络(CNN)将交互关系扩为复杂结构,捕获上下文信息,以欧氏距离算相似度。找到目标用户相似邻居集,用用户协同过滤预测评分,融合时间加权策略动态调整,生成推荐列表。测试表明,该算法归一化折损累计增益(NDCG)值高,平均绝对误差(MAE)和均方根误差(RMSE)低,推荐效果较理想。Due to the lack of sufficient interaction support,the recommendation accuracy is poor.To address this,a sparse data collaborative filtering recommendation algorithm based on knowledge graph was proposed.Extract the interaction relationship between users and items,a knowledge graph was constructed,and the entity relationships in the knowledge graph was used to extend the representation of users and items.Combining CNN networks,interactive relationships was expanded into complex structures,contextual information was captured,and similarity using Euclidean distance was calculate.A set of similar neighbors was found for the target user,user collaboration filtering was used to predict ratings,the fusion time weighting strategy was dynamically adjusted,and a recommendation list was generated.Tests have shown that the algorithm has high NDCG values,low MAE and RMSE values,and ideal recommendation performance.

关 键 词：知识图谱 稀疏数据 推荐算法 相似度 CNN网络 推荐精度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]