no code implementations • 10 Aug 2023 • Guyu Jiang, Xiaoyun Li, Rongrong Jing, Ruoqi Zhao, Xingliang Ni, Guodong Cao, Ning Hu
Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items.
no code implementations • 20 Sep 2022 • Shaochuan Lin, Yicong Yu, Xiyu Ji, Taotao Zhou, Hengxu He, Zisen Sang, Jia Jia, Guodong Cao, Ning Hu
In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i. e., in different geographical locations and at different times, user click behavior will change significantly.
no code implementations • 5 Jun 2022 • Guodong Cao, Zhibo Wang, Xiaowei Dong, Zhifei Zhang, Hengchang Guo, Zhan Qin, Kui Ren
However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.
no code implementations • 24 Jun 2021 • Peiyuan Zhu, XiaoFeng Wang, Zisen Sang, Aiquan Yuan, Guodong Cao
Hence, in this paper, we propose a context-aware heterogeneous graph attention network (CHGAT) to dynamically generate the representation of the user and to estimate the probability for future behavior.