Studying Long-Term User Behaviour Using Dynamic Time Warping for Customer Retention

被引：1
|
作者：
Gwalani, Harsha [1 ]
机构：
[1] Twitter, Jaipur, Rajasthan, India
来源：
WSDM'22: PROCEEDINGS OF THE FIFTEENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING | 2022年
关键词：
Dynamic Time Warping; Time Series Clustering; Customer Retention;
D O I：
10.1145/3488560.3510015
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
引用
收藏
页码：1643 / 1643
页数：1
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