Search Results for author: Weipeng Yan

Found 15 papers, 4 papers with code

Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels

1 code implementation CVPR 2023 Zixuan Ding, Ao Wang, Hui Chen, Qiang Zhang, Pengzhang Liu, Yongjun Bao, Weipeng Yan, Jungong Han

In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter.

Language Modelling Self-Supervised Learning +1

Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search

no code implementations20 Mar 2023 Binbin Wang, Mingming Li, Zhixiong Zeng, Jingwei Zhuo, Songlin Wang, Sulong Xu, Bo Long, Weipeng Yan

Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.


Adaptive Experimentation with Delayed Binary Feedback

1 code implementation2 Feb 2022 Zenan Wang, Carlos Carrion, Xiliang Lin, Fuhua Ji, Yongjun Bao, Weipeng Yan

Experiments show that the proposed method is more efficient for delayed feedback compared to various other approaches and is robust in different settings.

Multi-Armed Bandits valid

Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach

no code implementations28 May 2021 Carlos Carrion, Zenan Wang, Harikesh Nair, Xianghong Luo, Yulin Lei, Xiliang Lin, Wenlong Chen, Qiyu Hu, Changping Peng, Yongjun Bao, Weipeng Yan

In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior.


Probing Product Description Generation via Posterior Distillation

no code implementations2 Mar 2021 Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Zhuoye Ding, Yongjun Bao, Weipeng Yan, Yanyan Lan

To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews.

DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network

no code implementations24 Nov 2020 Junyou He, Guibao Mei, Feng Xing, Xiaorui Yang, Yongjun Bao, Weipeng Yan

More importantly, DADNN utilizes a single model for multiple scenes which saves a lot of offline training and online serving resources.

Click-Through Rate Prediction Transfer Learning

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

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

Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning

no code implementations3 Jun 2020 Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, Wen-Yun Yang

Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query.

Retrieval Semantic Retrieval

Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction

no code implementations22 Mar 2019 Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng Yan

To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i. e., high-level agent and low-level agent.

Hierarchical Reinforcement Learning Recommendation Systems +2

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