Profiling users by online shopping behaviors

被引：6
作者：
Yan, Huan [1 ]
Wang, Zifeng [1 ]
Lin, Tzu-Heng [1 ]
Li, Yong [1 ]
Jin, Depeng [1 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2018年 / 77卷 / 17期
关键词：
Social network; User behavior analytics; Data analysis;
D O I：
10.1007/s11042-017-5365-7
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Online shopping has been prevalent in our daily life. Profiling users and understanding their browsing behaviors are critical for enhancing shopping experience and maximizing sales revenue. In this paper, based on a one-month dataset recording 2 million users' 67 million online shopping and browsing logs, we seek to understand how users browse and shop products, and how distinct these behaviors are. We find that there exist dedicate groups of users that prefer certain product categories corresponding to similar demands. Moreover, distinct differences of behaviors exist in categories, where repetitive and targeted browsing are two major prevalent patterns.
引用
收藏
页码：21935 / 21945
页数：11
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