一种基于自适应学习率的推荐优化算法模型  

A Recommend Optimization Algorithm Model Based on Adaptive Learning Rate

作　　者：熊彬 贺春林[1] 周坤[1] XIONG Bin;HE Chunlin;ZHOU Kun(College of Computer Science,China West Normal University,Nanchong Sichuan 637009,China)

机构地区：[1]西华师范大学计算机学院

出　　处：《西华师范大学学报（自然科学版）》2019年第2期197-203,共7页Journal of China West Normal University(Natural Sciences)

基　　金：四川省教育厅重点项目(15ZA048);西华师范大学英才基金资助课题(17YC150);国家级大学生创新创业训练计划(201510638047)

摘　　要：在预测推荐系统中用户和项目构成的高维稀疏矩阵中的缺失值时,通常采用随机梯度下降算法对构造的隐因子(LF)模型进行求解,由于在求解过程中,学习速率始终保持不变,这使得在模型训练过程中模型的性能有所损失。因此,本文将构造一种带有自适应学习率的随机梯度下降算法的LF模型(ADA_LF)来处理推荐系统中的高维稀疏矩阵。采用大型工业数据集对模型进行实验测试,结果表明,采用ADA_SGD算法构建的LF模型在收敛速率、预测精度上都有明显提升,提高了模型的性能。In the prediction of missing value of recommender system with high-dimensional sparse matrix formed by users and items,Stochastic Gradient Descent algorithm is usually adopted to solve the latent factor (LF) model.However,model performance loss in the process of model training is occurred as a result of constant learning rate in the solution process.Hence,this paper proposes a stochastic gradient descent algorithm model with adaptive learning rate (ADA_SGD) to dispose high-dimensional sparse H of recommender system.Experimental tests of the model on large industrial data sets show that LF model constructed by ADA_SGD algorithm has greatly improved on convergence rate and prediction accuracy.Therefore,the performance of the model is greatly improved.

关 键 词：随机梯度下降 自适应学习率 高维稀疏矩阵 推荐系统 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]