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.


Multimodal Pretraining and Generation for Recommendation: A Tutorial, Jieming Zhu, Xin Zhou, Chuhan Wu, Rui Zhang, Zhenhua Dong. 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.

Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization, Qijiong Liu, Jiaren Xiao, Lu Fan, Jieming Zhu, Xiao-Ming Wu. 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.

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.

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.

Multi-Level Interaction Reranking with User Behavior History, Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu. In SIGIR 2022.

BARS: Towards Open Benchmarking for Recommender Systems, Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang. In SIGIR 2022.

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation, Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. In CIKM 2021.

Why Do We Click: Visual Impression-aware News Recommendation, Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu. In MM 2021.

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 RecSys-DLP 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

Achieving Cross Modal Generalization with Multimodal Unified Code 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.

Contrastive Learning with Positive-Negative Frame Mask for Music Representation, Dong Yao, Zhou Zhao, Shengyu Zhang, Jieming Zhu, Yudong Zhu, Rui Zhang, Xiuqiang He. In WWW 2022.

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.

Hierarchical Cross-Modal Graph Consistency Learning for Video-Text Retrieval, Weike Jin, Zhou Zhao, Pengcheng Zhang, Jieming Zhu, Xiuqiang He, Yueting Zhuang. In SIGIR 2021.

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

Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos, Zhu Zhang, Zhijie Lin, Zhou Zhao, Jieming Zhu, Xiuqiang He. In MM 2020.

Honors & Awards

Professional Services



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