no code implementations • 15 Mar 2024 • Yuanbo Gao, Peng Lin, Dongyue Wang, Feng Mei, Xiwei Zhao, Sulong Xu, Jinghe Hu
However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model.
no code implementations • 28 Jun 2023 • Jian Zhu, Congcong Liu, Pei Wang, Xiwei Zhao, Zhangang Lin, Jingping Shao
Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e. g. ads and recommendation systems.
no code implementations • 17 Apr 2023 • Congcong Liu, Fei Teng, Xiwei Zhao, Zhangang Lin, Jinghe Hu, Jingping Shao
Streaming data has the characteristic that the underlying distribution drifts over time and may recur.
no code implementations • 26 Jun 2022 • Han Xu, Hao Qi, Kunyao Wang, Pei Wang, Guowei Zhang, Congcong Liu, Junsheng Jin, Xiwei Zhao, Zhangang Lin, Jinghe Hu, Jingping Shao
In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items.
no code implementations • 11 May 2022 • Ye Tang, Xuesong Yang, Xinrui Liu, Xiwei Zhao, Zhangang Lin, Changping Peng
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on.
no code implementations • 8 Apr 2022 • Yinan Zhang, Pei Wang, Xiwei Zhao, Hao Qi, Jie He, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao
In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN).
no code implementations • 1 Apr 2022 • Congcong Liu, Yuejiang Li, Jian Zhu, Xiwei Zhao, Changping Peng, Zhangang Lin, Jingping Shao
Click-through rate (CTR) Prediction is of great importance in real-world online ads systems.
no code implementations • 1 Apr 2022 • Congcong Liu, Yuejiang Li, Fei Teng, Xiwei Zhao, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao
Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying.
no code implementations • 9 Nov 2021 • Jian Zhu, Congcong Liu, Pei Wang, Xiwei Zhao, Guangpeng Chen, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao
Learning to capture feature relations effectively and efficiently is essential in click-through rate (CTR) prediction of modern recommendation systems.
no code implementations • NeurIPS 2020 • Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan
First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.
no code implementations • 18 Jun 2020 • Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.