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.
1 code implementation • 8 Apr 2022 • Yinan Zhang, Pei Wang, Congcong Liu, 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 • 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.