面向非随机缺失数据的协同过滤评分方法  

Collaborative Score Prediction Method for Non-Random Missing Data

作　　者：古万荣 谢贤芬[2] 张子烨 毛宜军[1] 梁早清[1] 何亦琛 GU Wanrong;XIE Xianfen;ZHANG Ziye;MAO Yijun;LIANG Zaoqing;HE Yichen(School of Mathematics and Information, South China Agricultural University, Guangzhou 510642, Guangdong,China;School of Economics,Jinan University,Guangzhou 510632,Guangdong, China;School of Mathematics, South China University of Technology, Guangzhou 510640, Guangdong, China)

机构地区：[1]华南农业大学数学与信息学院,广东广州510642 [2]暨南大学经济学院,广东广州510632 [3]华南理工大学数学学院,广东广州510640

出　　处：《华南理工大学学报（自然科学版）》2021年第1期47-57,共11页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家重点研发计划项目(2017YFC1601701);广东省科技计划项目(2018A070712021);国家统计科学研究重点项目(2019LZ37);广东省哲学社会科学规划项目(GD18CXW01,GD19CGL34)。

摘　　要：大多数评分预测研究都是基于缺失值是随机的假设。然而,实际的线上推荐系统的评分矩阵的缺失数据都是非随机的。对缺失数据的错误假设会导致有偏差的参数估计和预测。为了提高非随机缺失评分矩阵填补的准确度,文中深入分析了用户和物品的评分矩阵的内在原理,提出了通过行或列变换将用户和物品的评分矩阵转变为等价的双边块对角矩阵,再在不同的分区块中分别应用矩阵分解方法进行分解和评分预测的方法,使得局部数据更新和分解成为现实。在公测数据集上的实验结果显示,文中方法可以提高评分填补效果,有效地解决非随机评分缺失问题,从而提高推荐系统的预测准确率。变换后的分块矩阵在分布式处理实验中也获得了较好的加速比,说明文中方法具有较好的应用可扩展性。Most score prediction studies are based on the assumption that the missing values are random.However,the missing data of the score matrix of the actual on-line recommendation system is non-random.Incorrect assumptions about the missing data can lead to biased parameter estimation and prediction.In order to improve the accuracy of non-random missing score matrix filling,the internal principle of user and item score matrix was analyzed in this paper.It presents a method to transform the score matrix of user and object into the equivalent bilateral block dia-gonal matrix by row or column transformation.Then the matrix decomposition method was applied to different blocks to decompose and predict the score,making local data update and decomposition become a reality.The experimental results on the public test dataset show that the proposed method can improve the score filling effect,solve the problem of non-random score missing effectively,and improve the prediction accuracy of the recommendation system.The improved block matrix also has a better speedup ratio in the distributed processing experiment,which shows that the proposed method has better scalability.

关 键 词：矩阵分解 推荐系统 奇异值分解 评分预测 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]