Profiling Big Data Users with Qualitative and Quantitative Fusion Methods

被引：0
|
作者：
Wenhan W. [1 ]
机构：
[1] Department of Library Information and Archives, Shanghai University, Shanghai
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 04期
关键词：
Big Data; Empirical Research; Fusion Method; User Portrait;
D O I：
10.11925/infotech.2096-3467.2021.0880
中图分类号：
学科分类号：
摘要：
[Objective] This research designed a new model of profiling big data users’portraits, aiming to address the fusion issue facing qualitative and quantitative methods. [Methods] We combined qualitative and quantitative methods to design the new model, which has a user value map based on sociological and psychological theories. Then, we used the Look-alike algorithm to build a map data label system, and used the K-Means clustering algorithm to processs the data. Finally, we interpret the clustered data. [Results] We examined our model with 200 million data points, and successfully divided young users into 20 groups. The total amount of data reached 17 million with 606 labels, which are better than the survey data. [Limitations] More research is needed to extract more original data, improve the subjective control of the user value map, as well as conduct heterogeneous data profiling. [Conclusions] The proposed model is of significance for related studies. © 2022, Chinese Academy of Sciences. All rights reserved.
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页码：108 / 119
页数：11
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