[1]钟志攀,袁仕芳,梁荣锋,等.SVD 推荐算法在传统服饰商城的应用[J].计算机技术与发展,2020,30(02):211-215.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 041]
　ZHONG Zhi-pan,YUAN Shi-fang,LIANG Rong-feng,et al.Application of SVD Recommendation Algorithm in Traditional Apparel Mall[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(02):211-215.[doi:10. 3969 / j. issn. 1673-629X. 2020. 02. 041]
点击复制

SVD 推荐算法在传统服饰商城的应用()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年02期
页码:
211-215
栏目:
应用开发研究
出版日期:
2020-02-10

文章信息/Info

Title:
Application of SVD Recommendation Algorithm in Traditional Apparel Mall
文章编号:
1673-629X(2020)02-0211-05
作者:
钟志攀; 袁仕芳; 梁荣锋; 朱翡虹
五邑大学 数学与计算科学学院,广东 江门 529020
Author(s):
ZHONG Zhi-pan; YUAN Shi-fang; LIANG Rong-feng; ZHU Fei-hong
School of Mathematics and Computational Science,Wuyi University,Jiangmen 529020,China
关键词:
SVD推荐算法; 推荐系统; 服饰商城; 最速下降算法; 奇异值分解
Keywords:
SVD recommendation algorithm; recommendation system; clothing mall; steepest descent algorithm; singular value decompo sition
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 02. 041
摘要:
随着互联网的普及和信息技术的发展,人们日常生活可以接触到的信息越来越丰富,产生信息数据的速度也越来 越快。 在拥有海量信息数据的同时,过量的信息不可避免地导致了“信息过载冶问题,而推荐系统正是解决这个问题的有 效方法之一。 首先介绍了奇异值分解(SVD)和SVD推荐算法之间的联系,然后叙述了SVD推荐算法的思路并给出SVD 算法模型的最优损失函数。 在此基础上,建立一个传统服饰商城的推荐系统,利用158组样本数据测试该系统,得出推荐 系统的均方根误差为0.9994,平方绝对值误差为0.7366。 此外还计算了样本数据的误差百分比,结果表明绝大多数测试 样本的误差百分比在30%以内。 基于SVD推荐模型的特点和实验结果得出,SVD推荐模型适用于小型电商平台的商城 用户的个性化推荐。
Abstract:
With the popularity of the Internet and the development of information technology,the information that people can access in daily life is becoming more and more abundant,and the speed of generating information data is getting faster and faster. While we have a huge amount of information data, excessive information inevitably leads to the problem of “information overload”,and the recommendation system is one of the effective methods to solve this problem. In this study,the relationship between singular value decomposition(SVD) and SVDrecommendation algorithm isintroduced firstly, and then the idea of SVD recommendation algorithm is described and the optimal loss function of the SVD algorithm model is given. Based on that,a recommendation system of traditional clothing store is established. The system is tested with 158 sets of sample data,of which the root mean square error is 0.9994 and the squared absolute error is 0.7366. In addition,we also calculate the error percentage of the sample data. The results show that the error percentage of most test samples is within 30%. Based on the characteristics of the SVD recommendation model and the experimental results,it is concluded that the SVD recommendation model is suitable for personalized recommendation of mall users on small ecommerce platforms.

相似文献/References:

[1]邵延振 蒙韧 袁鼎荣 李新友.基于Web结构分区的协同过滤推荐算法研究[J].计算机技术与发展,2010,(06):67.
　SHAO Yan-zhen,MENG Ren,YUAN Ding-rong,et al.Collaborative Filtering Recommendation Algorithm Research Based on Web Blocks[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2010,(02):67.
[2]曹毅 贺卫红.基于内容过滤的电子商务推荐系统研究[J].计算机技术与发展,2009,(06):182.
　CAO Yi,HE Wei-hong.Research on E- Commerce Recommender System Based on Content - Based Filtering[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2009,(02):182.
[3]赵鹏 蔡庆生 王清毅.一种用于文章推荐系统中的用户模型表示方法[J].计算机技术与发展,2007,(01):4.
　ZHAO Peng,CAI Qing-sheng,WANG Qing-yi.A Novel Representation of User Profile in Document Recommendation System[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2007,(02):4.
[4]高静 应吉康.基于人工免疫系统的推荐系统[J].计算机技术与发展,2007,(05):180.
　GAO Jing,YING Ji-kang.A Recommendation System Based on Artificial Immune System[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2007,(02):180.
[5]游文 叶水生.电子商务推荐系统中的协同过滤推荐[J].计算机技术与发展,2006,(09):70.
　YOU Wen,YE Shui-sheng.A Survey of Collaborative Filtering Algorithm Applied in E- commerce Recommender System[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2006,(02):70.
[6]华铨平.基于FNN的家纺产品个性化推荐系统的研究[J].计算机技术与发展,2011,(09):183.
　HUA Quan-ping.Research on Personalized Recommendation System of Textile Products Used by Family Based on FNN[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2011,(02):183.
[7]马言春 彭志平.基于市场机制的云服务管理研究[J].计算机技术与发展,2012,(03):214.
　MA Yan-chun,PENG Zhi-ping.Research on Cloud Service Management Based on Market Mechanism[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2012,(02):214.
[8]刘厚良.网络协同戏剧中个性化戏剧资源推荐系统[J].计算机技术与发展,2012,(08):25.
　LIU Hou-liang.Personalized Drama Resource Recommendation System in Cooperative Play[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2012,(02):25.
[9]陆晓敏 崇志宏 陈国庆.基于本体的商品推荐方法[J].计算机技术与发展,2012,(10):10.
　LU Xiao-min,CHONG Zhi-hong,CHEN Guo-qing.Product Recommending Method Based on Ontology[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2012,(02):10.
[10]范虎,花伟伟.协同过滤推荐算法的研究与改进[J].计算机技术与发展,2013,(09):66.
　FAN Hu[],HUA Wei-wei[].Research and Improvement of Collaborative Filtering Recommendation Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2013,(02):66.

备注/Memo

备注/Memo:
钟志攀,袁仕芳,梁荣锋,朱翡虹
更新日期/Last Update: 2020-02-10