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Xiaoqiang Zhu
Researcher at Alibaba Group
Publications - 61
Citations - 3661
Xiaoqiang Zhu is an academic researcher from Alibaba Group. The author has contributed to research in topics: Display advertising & Computer science. The author has an hindex of 17, co-authored 52 publications receiving 1792 citations. Previous affiliations of Xiaoqiang Zhu include Tsinghua University & University of Texas Southwestern Medical Center.
Papers
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Proceedings ArticleDOI
Deep Interest Network for Click-Through Rate Prediction
Guorui Zhou,Xiaoqiang Zhu,Chenru Song,Ying Fan,Han Zhu,Xiao Ma,Yan Yanghui,Junqi Jin,Han Li,Kun Gai +9 more
TL;DR: A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.
Journal ArticleDOI
Deep Interest Evolution Network for Click-Through Rate Prediction
TL;DR: Wang et al. as discussed by the authors proposed a deep interest evolution network (DIEN) for CTR prediction, which considers the changing trend of the interest and introduces an auxiliary loss to supervise interest extracting at each step.
Proceedings ArticleDOI
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
TL;DR: This paper model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion, which is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
Proceedings ArticleDOI
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
TL;DR: This paper faces directly the challenge of long sequential user behavior modeling and proposes a novel memory-based architecture named MIMN (Multi-channel user Interest Memory Network) to capture user interests from long sequential behavior data, achieving superior performance over state-of-the-art models.
Journal ArticleDOI
An Image Encryption Algorithm Based on Josephus Traversing and Mixed Chaotic Map
TL;DR: Security analysis indicates that the new chaotic image encryption scheme, which employs Josephus traversing and mixed chaotic map, is effective, which can resist common attacks.