基于Skyline Query的高声誉用户识别方法研究

刘晓露; 贾书伟; 王建民; 刘晓露; 贾书伟; 王建民

doi:10.13306/j.1672-3813.2018.02.008

复杂系统与复杂性科学 >

2018 , Vol. 15 >Issue 2: 62 - 70

DOI: https://doi.org/10.13306/j.1672-3813.2018.02.008

基于Skyline Query的高声誉用户识别方法研究

刘晓露 ,
贾书伟 ,
王建民 ,
刘晓露 ,
贾书伟 ,
王建民

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1.复旦大学a.经济学院,b.泛海国际金融学院,上海 200433;
2.河南农业大学信息与管理科学学院,郑州 450002;
3.安徽理工大学经济与管理学院,安徽淮南 232001

刘晓露(1989-),女,山东东营人,博士,主要研究方向为复杂网络、在线用户行为分析。

收稿日期: 2018-04-25

网络出版日期: 2019-01-09

基金资助

国家自然科学基金项目(71473001);教育部人文社科青年项目(18YJC630102);中国博士后科学基金面上资助项目(2018M630404)

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Identifying High Reputation Users Based on Skyline Query

LIU Xiaolu ,
JIA Shuwei ,
WANG Jianmin ,
LIU Xiaolu ,
JIA Shuwei ,
WANG Jianmin

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1.a.School of Economics, b.Fanhai International School of Finance, Fudan University, Shanghai 200433, China;
2.College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China;
3.School of Economics and Management, Anhui University of Science and Technology , Huainan 232001, China

Received date: 2018-04-25

Online published: 2019-01-09

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摘要

用户声誉的研究对于互联网金融和电子商务的健康发展具有重要意义,是在线用户行为分析中一个重要的研究方向。在线用户评分系统中研究学者提出了许多声誉度量算法,然而不同方法度量用户声誉的思想和角度是不同的。为了在海量数据中对用户声誉有一个总体的认识,提出一种基于Skyline Query的高声誉用户识别方法。将已有的几种声誉度量方法进行分类,综合选取代表性的算法得到的用户声誉用Skyline查询方法找到的集合Skyline中不被其他用户所支配的用户,即为高声誉用户。同时分析不同时间段上得到的集合Skyline中高声誉用户的规律。本文综合多种声誉度量方法从定性角度对声誉进行应用研究,拓宽了用户声誉研究的广度。

关键词： 在线评分系统; 用户声誉; Skyline Query; 高声誉用户

本文引用格式

刘晓露 , 贾书伟 , 王建民 , 刘晓露 , 贾书伟 , 王建民 . 基于Skyline Query的高声誉用户识别方法研究[J]. 复杂系统与复杂性科学, 2018 , 15(2) : 62 -70 . DOI: 10.13306/j.1672-3813.2018.02.008

Abstract

User reputation is of great significance for healthy development of Internet finance and E-commerce, and it is an important research hotspot in online user's behavior analysis. Many reputation measurement methods have proposed by scholars for online rating systems. However, different reputation measurement methods are designed form different perspectives. In order to have a general understanding of user's reputation, Skyline query is introduced to identify high-reputation users in this paper.By classifying the current methods for measuring user's reputation based on clustering methods, we select a representative algorithm in each class and the user's reputation by the selected algorithms are calculated with Skyline Query. The users in the found Skyline set (not being dominated by other users) are the high-reputation users. We also analyze the rules of high-reputation users within different time periods.This paper carries out applied research on reputation from a qualitative point of view by combining multiple reputation measurement methods, broadening the breadth of the research on user reputation.

Key words： online rating systems; user's reputation; Skyline Query; high reputation user

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