本文選題：商戶評分數(shù)據(jù) + 關聯(lián)分析��； 參考：《鄭州大學》2017年碩士論文

【摘要】：當今社會,人們在出門選擇商家或者購買商品之前,往往會先查看電商網(wǎng)站上的商戶評分信息,從而判斷這家商戶的服務或產(chǎn)品質(zhì)量等情況,以便做出消費決策。人們經(jīng)常會使用網(wǎng)站上的商戶評分信息進行消費參考,而很少有人研究這些商戶評分信息本身。對商戶的評分數(shù)據(jù)進行合理的分析研究,從而發(fā)現(xiàn)數(shù)據(jù)之間的關聯(lián)關系,有助于用戶判斷商戶評分數(shù)據(jù)的可信度,做出合理的消費選擇。在本文研究中,筆者從關聯(lián)分析的角度出發(fā),分析這些電商網(wǎng)站上的商戶評分數(shù)據(jù)。首先介紹商戶的評分數(shù)據(jù)、關聯(lián)分析、相關分析、回歸分析的基礎概念與方法,然后利用相關分析和回歸分析的方法,設計一套適用于電商網(wǎng)站商戶評分數(shù)據(jù)的關聯(lián)分析方法,該方法分為數(shù)據(jù)采集、數(shù)據(jù)清洗和關聯(lián)分析三個步驟,其中關聯(lián)分析部分又分為三個方面,分別是商戶評分數(shù)據(jù)的相關分析、線性回歸分析和虛擬回歸分析,利用相關分析找出商戶各項評分之間或商戶評分與人均消費之間的關聯(lián),利用線性回歸方法分析商戶總評分與各項評分之間的關系,利用虛擬回歸分析探索不同類別對于商戶評分的影響,找出商戶評分數(shù)據(jù)之間的關聯(lián)關系。為了驗證這套商戶評分數(shù)據(jù)的關聯(lián)分析方法,筆者采集5個地市的3700條餐飲商戶評分數(shù)據(jù),對這套關聯(lián)分析方法進行實證研究。利用相關分析發(fā)現(xiàn)商戶各項評分之間、人均消費和商戶各項評分之間的關聯(lián)關系,找出用戶的打分習慣,分析人均消費與商戶各方面服務是否存在關聯(lián);利用線性回歸分析計算出商戶總評分與各方面評分之間的關聯(lián)關系,判斷商戶總評分數(shù)據(jù)的可信度,了解哪些因素更能影響商戶的總評分;利用虛擬回歸分析找出地域?qū)θ藗兿埠貌讼档挠绊�。查看�?shù)據(jù)的處理效果,驗證這套商戶評分數(shù)據(jù)的關聯(lián)分析方法。對于消費者來說,可以驗證日常生活中使用的商戶評分數(shù)據(jù)是否可信,增加人們對這些評分數(shù)據(jù)的了解;對于商戶來說,可以幫助他們找到消費者的喜好,助其發(fā)展經(jīng)營。
[Abstract]:In today's society, people often check the score information on e-commerce websites before going out to choose merchants or buy goods, so as to judge the service or product quality of the merchants in order to make consumer decisions. People often use the business rating information on the website for consumer reference, but few people study the information itself. This paper makes a reasonable analysis and research on the score data of the merchant, and finds out the correlation between the data, which is helpful for the user to judge the credibility of the scoring data and make a reasonable consumption choice. In this study, the author analyzes the score data of these e-commerce websites from the perspective of correlation analysis. This paper first introduces the basic concepts and methods of scoring data, association analysis, correlation analysis and regression analysis of merchants, and then designs a set of association analysis methods suitable for the score data of merchants on e-commerce websites by using the methods of correlation analysis and regression analysis. The method is divided into three steps: data acquisition, data cleaning and association analysis. Correlation analysis was used to find out the relationship between the score of the merchant and the consumption per capita, and the linear regression method was used to analyze the relationship between the total score of the merchant and the score. Virtual regression analysis is used to explore the influence of different categories on the score of merchants and to find out the correlation between the data. In order to verify the correlation analysis method, the author collected 3700 restaurant rating data from 5 prefectures, and made an empirical study on the correlation analysis method. By using the correlation analysis, we find out the relationship between the scores, the consumption per capita and the scores, find out the scoring habits of the users, and analyze whether there is a correlation between the consumption per capita and the various aspects of services. The linear regression analysis is used to calculate the correlation between the total score of the merchant and the score of all aspects, to judge the reliability of the data of the total score of the merchant, and to find out which factors can affect the total score of the merchant. Virtual regression analysis was used to find out the influence of region on people's favorite cuisine. Check the effect of data processing, verify this set of business score data association analysis method. For consumers, it can verify the credibility of the business score data used in daily life, increase people's understanding of these scoring data; for businesses, it can help them to find consumer preferences and help them to develop business.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F724.6;F274

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