Email · jiemingzhu@ieee.org

I am currently a lead researcher at Huawei Noah's Ark Lab (AI Lab). Before that, I obtained my Ph.D. degree in Computer Science and Engineering from The Chinese University of Hong Kong in 2016, supervised by Prof. Michael R. Lyu. I received the B.Eng degree from Beijing University of Posts and Telecommunications. My recent research focus is on building and applying practical machine learning algorithms (especially ranking, NLP and multimodal learning) for industrial-scale recommender systems, with a goal to help better discover users' interests and serve their needs. Our team has launched many self-designed ML algorithms on Huawei's products like News Feeds, Microvideo Stream, Music App, App Store, PPS Ads, etc.

I am always looking for students and interns who are interested in recommender systems, LLMs, or multimodal AI. Please feel free to reach out if you are interested!



The Chinese University of Hong Kong

PhD in Computer Science and Engineering
Aug 2011 - Jan 2016

Imperial College London

Visiting PhD Student
May 2015 - Nov 2015

Beijing University of Posts and Telecommunications

Bachelor of Engineering
Sep 2007 - Jun 2011


Lead Researcher

Huawei Noah's Ark Lab, Shenzhen, China
Mar 2020 - Present


Huawei Noah's Ark Lab & Huawei 2012 Labs, Shenzhen, China

Dec 2016 - Mar 2020

Postdoc Fellow

The Chinese University of Hong Kong, Hong Kong

Jan 2016 - Dec 2016

Research Intern

Microsoft Research Lab, Beijing, China

May 2013 - Sep 2013


My current research focuses mainly on recommender systems and pretrained multimodal models for understanding and generation. I have published 70+ papers in top conferences such as NeurIPS, SIGIR, KDD, WWW, ACL, CVPR, MM, etc., which have received . Please see below for some recent publications grouped by research topics.


Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time, Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, Ji-Rong Wen. In KDD 2024.

EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration, Ye Wang, Jiahao Xun, Minjie Hong, Jieming Zhu, Tao Jin, Wang Lin, Haoyuan Li, Linjun Li, Yan Xia, Zhou Zhao, Zhenhua Dong. In KDD 2024.

Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey, Qijiong Liu*, Jieming Zhu*✝, Yanting Yang, Quanyu Dai, Zhaocheng Du, Xiao-Ming Wu, Zhou Zhao, Rui Zhang, Zhenhua Dong. In KDD 2024.

Discrete Semantic Tokenization for Deep CTR Prediction, Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan, Xiao-Ming Wu. In WWW 2024.

LightCS: Selecting Quadratic Feature Crosses in Linear Complexity, Zhaocheng Du, Junhao Chen, Qinglin Jia, Chuhan Wu, Jieming Zhu, Zhenhua Dong, Ruiming Tang. In WWW 2024.

FINAL: Factorized Interaction Layer for CTR Prediction, Jieming Zhu, Qinglin Jia, Guohao Cai, Quanyu Dai, Jingjie Li, Zhenhua Dong, Ruiming Tang, Rui Zhang. In SIGIR 2023.

Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation, Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang. In SIGIR 2023.

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction, Kelong Mao*, Jieming Zhu*, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong. In AAAI 2023.

ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop, Jieming Zhu, Guohao Cai, Junjie Huang, Zhenhua Dong, Ruiming Tang, Weinan Zhang. In KDD 2023.

LLMs for Recommendation

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models, Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu. In DLP-RecSys 2023. [Best Paper Award]

Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation, Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiaoming Wu. In COLING 2022.

MINER: Multi-Interest Matching Network for News Recommendation, Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, Qun Liu. In ACL 2022.

UNBERT: User-News Matching BERT for News Recommendation, Qi Zhang, Jingjie Li, Qinglin Jia, Chuyuan Wang, Jieming Zhu, Zhaowei Wang, Xiuqiang He. In IJCAI 2021.

Personalized AI

PMG: Personalized Multimodal Generation with Large Language Models, Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, Xi Xiao. In WWW 2024.

Multimodal AI

Unlocking the Potential of Multimodal Unified Discrete Representation through Training-Free Codebook Optimization and Hierarchical Alignment, Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Jieming Zhu, Zhenhua Dong, Zhou Zhao. In Arxiv 2024.

MART: Learning Hierarchical Music Audio Representations with Part-Whole Transformer, Dong Yao*, Jieming Zhu*, Jiahao Xun, Shengyu Zhang, Zhou Zhao, Liqun Deng, Wenqiao Zhang, Zhenhua Dong, Xin Jiang. In WWW 2024.

Achieving Cross Modal Generalization with Multimodal Unified Representation, Yan Xia, Hai Huang, Jieming Zhu, Zhou Zhao. In NeurIPS 2023.

Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks, Haoyi Duan, Yan Xia, Mingze Zhou, Li Tang, Jieming Zhu, Zhou Zhao. In NeurIPS 2023.

DisCover: Disentangled Music Representation Learning for Cover Song Identification, Jiahao Xun, Shengyu Zhang, Yanting Yang, Jieming Zhu, Liqun Deng, Zhou Zhao, Zhenhua Dong, Ruiqi Li, Lichao Zhang, Fei Wu. In SIGIR 2023.

M4Singer: a Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus, Lichao Zhang, Ruiqi Li, Shoutong Wang, Liqun Deng, Jinglin Liu, Yi Ren, Jinzheng He, Rongjie Huang, Jieming Zhu, Xiao Chen, Zhou Zhao. In NeurIPS 2022.

Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding, Zhu Zhang, Zhou Zhao, Zhijie Lin, Jieming Zhu, Xiuqiang He. In NeurIPS 2020.

Honors & Awards

Professional Services



  • Area Chair: NeurIPS'24, NeurIPS'23, Session Chair: SIGIR-AP'23
  • Senior Program Committee: SIGIR'24
  • Program Committee & Reviewer: NeurIPS, CVPR, KDD, SIGIR, WWW, AAAI for many years.