Accelerating DIN Model for Online CTR Prediction with Data Compression

科研成果详情

题名	Accelerating DIN Model for Online CTR Prediction with Data Compression
作者	Feng，Yitian 1; Zhu，Shengxin 2; Ou，Yichen 1
发表日期	2022
会议录名称	2022 7th International Conference on Big Data Analytics, ICBDA 2022
页码	84-89
摘要	As the key task of recommender systems, the click-Through rate(CTR) prediction is to predict the probability of users clicking on a specific product. It is often costly due to the big data sets. In this paper, we apply some data compression technology to accelerate CTR prediction. By casting the data format to some more memory-efficient format, we can significantly improve a popular recommender method online.
关键词	CTR prediction data compression recommender system
DOI	10.1109/ICBDA55095.2022.9760313
URL	查看来源
语种	英语English
Scopus入藏号	2-s2.0-85129526069
引用统计
文献类型	会议论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/11499
专题	北师香港浸会大学
通讯作者	Zhu，Shengxin
作者单位	1.BNU-HKBU United International College,Division of Science and Technology,Zhuhai,China 2.Beijing Normal University,Research Center for Mathematics,Zhuhai,China
第一作者单位	北师香港浸会大学
推荐引用方式 GB/T 7714	Feng，Yitian,Zhu，Shengxin,Ou，Yichen. Accelerating DIN Model for Online CTR Prediction with Data Compression[C], 2022: 84-89.