no code implementations • 6 May 2024 • Wenhao Zhu, Guojie Song, Liang Wang, Shaoguo Liu
Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and demonstrating promising performance and expressive power.
no code implementations • 15 May 2023 • Penghui Wei, Hongjian Dou, Shaoguo Liu, Rongjun Tang, Li Liu, Liang Wang, Bo Zheng
We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms.
no code implementations • 10 Mar 2023 • Xuanhua Yang, Jianxin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng
Multi-task learning (MTL) has been widely applied in online advertising and recommender systems.
no code implementations • 6 Feb 2023 • Shanlei Mu, Penghui Wei, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng
In this paper, we propose a Hybrid Contrastive Constrained approach (HC^2) for multi-scenario ad ranking.
no code implementations • 6 Feb 2023 • Penghui Wei, Yongqiang Chen, Shaoguo Liu, Liang Wang, Bo Zheng
In a whole delivery period, advertisers usually desire a certain impression count for the ads, and they also expect that the delivery performance is as good as possible (e. g., obtaining high click-through rate).
1 code implementation • ICCV 2023 • Yaopei Zeng, Lei Liu, Li Liu, Li Shen, Shaoguo Liu, Baoyuan Wu
In particular, a proxy is derived from the accumulated gradients uploaded by the clients after local training, and is shared by all clients as the class prior for re-balance training.
no code implementations • 21 Nov 2022 • Zixuan Xu, Penghui Wei, Shaoguo Liu, Weimin Zhang, Liang Wang, Bo Zheng
Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains.
no code implementations • 29 Aug 2022 • Zihan Lin, Xuanhua Yang, Xiaoyu Peng, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng
For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance.
no code implementations • 27 Jun 2022 • Xuanhua Yang, Xiaoyu Peng, Penghui Wei, Shaoguo Liu, Liang Wang, Bo Zheng
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems.
no code implementations • 30 May 2022 • Penghui Wei, Shaoguo Liu, Xuanhua Yang, Liang Wang, Bo Zheng
Current bundle generation studies focus on generating a combination of items to improve user experience.
no code implementations • NAACL (ACL) 2022 • Penghui Wei, Xuanhua Yang, Shaoguo Liu, Liang Wang, Bo Zheng
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR).
no code implementations • 15 May 2022 • Penghui Wei, Weimin Zhang, Ruijie Hou, Jinquan Liu, Shaoguo Liu, Liang Wang, Bo Zheng
Calibration techniques aims to post-process model predictions to posterior probabilities.
no code implementations • 20 Jan 2022 • Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang, Bo Zheng
Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels.
no code implementations • 17 May 2021 • Xu Ma, Pengjie Wang, Hui Zhao, Shaoguo Liu, Chuhan Zhao, Wei Lin, Kuang-Chih Lee, Jian Xu, Bo Zheng
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted.