基于信任关系的非线性表征潜在因子模型  

Nonlinear Representation Latent Factor Decomposition Model Based on Trust Relationship

作　　者：潘天艺 宋燕[1] PAN Tianyi;SONG Yan(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2025年第2期53-61,共9页Electronic Science and Technology

基　　金：国家自然科学基金(62073223);上海市自然科学基金(22ZR1443400)。

摘　　要：针对高维稀疏无向网络挖掘实体间潜在关联信息的表征能力较弱和计算效率较低的问题,文中在社交推荐模型框架下提出了一种基于信任关系的非负非线性表征潜在因子模型。该模型通过非线性映射塑造潜在矩阵的特征空间,既保证了目标矩阵的非负性,又提高了模型的表征能力。通过在模型训练的目标函数中引入图拉普拉斯正则化项保证了信任关系映射前后的结构一致性。基于6个公开数据集的对比实验结果表明,所提模型较其他模型具有明显的优越性。In view of the problems of weak characterization ability and low computational efficiency of high-dimensional sparse undirected networks for mining potential correlation information among entities,this study proposes a non-negative nonlinear characterization latent factor model based on trust relationship under the framework of social recommendation model.The model shapes the feature space of the latent matrix through nonlinear mapping,which guarantees not only the non-negativity of the target matrix,but also improves the characterization ability of the model.With the introduction of the graph Laplace regularization term in the objective function of the model training,the structural consistency between before and after the mapping of the trust relationship is ensured.The experimental results based on six public data sets show that the proposed model has obvious superiority over other models.

关 键 词：高维稀疏无向网络 社交推荐模型 信任关系 非负非线性 特征空间 图拉普拉斯正则化 潜在因子模型 小批量梯度下降法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]