基于信誉度的商品推荐建模与仿真分析  

Modeling and Simulation Analysis of Commodity Recommendation based on Reputation

作　　者：王小豪 孙彦武[2] 胡浩明 王蕊蕊 乐光学[1] WANG Xiao-hao;SUN Yan-wu;HU Hao-ming;WANG Rui-rui;YUE Guang-xue(College of Mathematics,Physics and Information Engineering,Jiaxing University,Jiaxing 314001,China;College of Foreign Language,Jiaxing University,Jiaxing 314001,China)

机构地区：[1]嘉兴学院数理与信息工程学院,浙江嘉兴314001 [2]嘉兴学院外国语学院,浙江嘉兴314001

出　　处：《电脑知识与技术》2019年第5期294-296,共3页Computer Knowledge and Technology

摘　　要：商品推荐的发展不仅是大数据和云计算技术发展的成果体现,也是电子商务行业的发展趋向,商品推荐是电商行业的关键技术,是提升电商行业经济效益的有力途径之一。通过在电商行业应用商品推荐技术,能够实现快速搜索和推送目标商品,优化用户购物体验,推动新时代电商行业的进一步发展。因此,该文通过对现有商品推荐算法的分析和研究,尝试提出了基于商铺和商品信誉度的推荐算法。通过问卷调查确定影响商铺和商品信誉度的关键指标,运用Entropy算法和欧几里德距离分别计算商品信誉度和相似度,最后根据信誉度和相似度完成商品推荐。通过建模和仿真分析,该算法在一定程度上提高了商品推荐的可信度和精确度。The development of commodity recommendation is not only the result of the development of big data and cloud computing technology,but also the development trend of e-commerce industry.Commodity recommendation is the key technology of e-commerce industry and one of the powerful ways to improve the economic benefits of e-commerce industry.By applying commodity recommendation technology in the e-commerce industry,it is possible to quickly search and push target commodity,optimize the user's shopping experience,and promote the further development of the e-commerce industry in the new era.Therefore,this paper at-tempts to propose a recommendation algorithm based on the reputation of shops and commodities through the analysis and research of existing commodity recommendation algorithms.Through the questionnaire survey to determine the key indicators affecting the shop and commodity credibility,using Entropy algorithm and Euclidean distance to calculate the commodity credibility and similarity,and finally complete the commodity recommendation based on credibility and similarity.Through modeling and simulation analysis,the algorithm improves the credibility and accuracy of commodity recommendation to a certain extent.

关 键 词：商品推荐 商铺/商品信誉度 ENTROPY 欧几里德距离 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]