Search Results for author: Xiwei Zhao

Found 11 papers, 0 papers with code

Confidence Ranking for CTR Prediction

no code implementations28 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.

Click-Through Rate Prediction Recommendation Systems

PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving

no code implementations26 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.

Click-Through Rate Prediction Re-Ranking +1

NDGGNET-A Node Independent Gate based Graph Neural Networks

no code implementations11 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.

Graph Classification Link Prediction +1

IA-GCN: Interactive Graph Convolutional Network for Recommendation

no code implementations8 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).

Collaborative Filtering Recommendation Systems

On the Adaptation to Concept Drift for CTR Prediction

no code implementations1 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.

Click-Through Rate Prediction Incremental Learning +1

Dynamic Parameterized Network for CTR Prediction

no code implementations9 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.

Click-Through Rate Prediction Recommendation Systems

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

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.

Click-Through Rate Prediction

Category-Specific CNN for Visual-aware CTR Prediction at JD.com

no code implementations18 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.

Click-Through Rate Prediction

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