基于改进的KNN算法的家政服务行业人单匹配  

Person Service Items Fit Based on the Novel KNN Algorithm in Household Service Industry

作　　者：朱虹影 刘峰涛[1] 

机构地区：[1]东华大学旭日工商管理学院,上海

出　　处：《服务科学和管理》2020年第1期55-60,共6页Service Science and Management

摘　　要：互联网时代下家政服务行业普遍存在顾客满意度低,服务人员专业性低等问题,阻碍了行业发展的脚步。探究其中的原因可以发现家政服务员与服务项目不匹配是其最根本的原因。基于这一背景,本文利用大数据环境下KNN这一距离类算法对家政服务员与服务项目进行人单匹配,并在传统KNN算法的基础之上通过样本距离权重将其进行改进,得到改进的人单匹配模型。实验表明,改进的人单匹配模型相对于基于传统KNN算法的人单匹配模型,准确率达到69.36%,分类结果更好,误差率更低,可以很好地将服务员与服务项目相匹配,促进家政服务员的专业化培训,提升顾客满意度,推动家政服务行业的长远发展。In the Internet era, the domestic service industry generally has problems such as low customer sa-tisfaction and low professionalism of housekeeper, which hinders the development of the industry. Under these circumstances, the reasons for this can be found that the mismatching between the domestic housekeeper and the service items is the most fundamental reason. Based on this back-ground, this paper uses KNN, a distance-based algorithm in the big data environment, to match the housekeeping staff with the service project, and improve it by the sample distance weight based on the traditional KNN algorithm. An improved person service items fit model is obtained. Expe-riments show that the improved person service items fit model has an accuracy rate of 69.36% compared with the traditional person service items fit model based on the traditional KNN algo-rithm. The classification result is better and the error rate is lower. It can match the housekeeper and the service project well and promote the professional training of housekeeping staff, so that it will enhance customer satisfaction and promotes the long-term development of the domestic ser-vice industry.

关 键 词：人单匹配 KNN算法 家政服务 准确率比较 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]