Mining distinguishing customer focus sets from online customer reviews

被引：4
作者：
Duan, Lei [1 ]
Liu, Lu [1 ]
Dong, Guozhu [2 ]
Nummenmaa, Jyrki [3 ]
Wang, Tingting [1 ]
Qin, Pan [1 ]
Yang, Hao [1 ]
机构：
[1] Sichuan Univ, Sch Comp Sci, Chengdu, Sichuan, Peoples R China
[2] Wright State Univ, Dept Comp Sci & Engn, Dayton, OH 45435 USA
[3] Univ Tampere, Fac Nat Sci, Tampere, Finland
来源：
COMPUTING | 2018年 / 100卷 / 04期
基金：
芬兰科学院; 中国博士后科学基金; 中国国家自然科学基金;
关键词：
Distinguishing customer focus; Decision support; Data mining; PATTERNS;
D O I：
10.1007/s00607-018-0601-1
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
With the development of e-commerce, online shopping becomes increasingly popular. Very often, online shopping customers read reviews written by other customers to compare similar items. However, the number of customer reviews is typically too large to look through in a reasonable amount of time. To extract information that can be used for online shopping decision support, this paper investigates a novel data mining problem of mining distinguishing customer focus sets from customer reviews. We demonstrate that this problem has many applications, and at the same time, is challenging. We present dFocus-Miner, a mining method with various techniques that makes the mined results interpretable and user-friendly. Moreover, we propose a visualization design to display the results of dFocus-Miner. Our experimental results on real world data sets verify the effectiveness and efficiency of our method.
引用
收藏
页码：335 / 351
页数：17
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