Email ยท 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 matching, ranking, NLP and multimodal pretraining) 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, NLP, or multimodal pretraining. Please feel free to reach out if you are interested!



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


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


My current research focuses mainly on recommender systems and multimodal understanding related techniques. I have 50+ publicatoins in total, including papers in NeurIPS, ACL, SIGIR, CVPR, MM, WWW, etc., which have received 3000+ citations. See my recent publications below grouped by research topics. For the full publications, please visit my Google Scholar.

CTR Prediction

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

ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems, Guohao Cai, Jieming Zhu (co-first author), Quanyu Dai, Zhenhua Dong, Xiuqiang He, Ruiming Tang, Rui Zhang. In SIGIR 2022.

LCD: Adaptive Label Correction for Denoising Music Recommendation, Quanyu Dai, Yalei Lv, Jieming Zhu, Junjie Ye, Zhenhua Dong, Rui Zhang, Shu-Tao Xia, Ruiming Tang. In CIKM 2022.

Open Benchmarking for Click-Through Rate Prediction, Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. In CIKM 2021.

Ensembled CTR Prediction via Knowledge Distillation, Jieming Zhu, Jinyang Liu, Weiqi Li, Jincai Lai, Xiuqiang He, Liang Chen, Zibin Zheng. In CIKM 2020.

Candidate Item Matching

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

Cross-Batch Negative Sampling for Training Two-Tower Recommenders, Jinpeng Wang, Jieming Zhu, Xiuqiang He. In SIGIR 2021.

Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach, Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He. In SIGIR 2020.


PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation, Yi Li, Jieming Zhu (co-first author), Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang. In WWW 2022.

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.


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.

Multimodal Learning

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.

Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks, Wenwen Pan, Haonan Shi, Zhou Zhao, Jieming Zhu, Xiuqiang He, Zhigeng Pan, Lianli Gao, Jun Yu, Fei Wu, Qi Tian. In CVPR 2022.

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.

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


DataFun Summit 2021 Talk: Research and Practice on Pretrained Models for News Feeds Recommendation at Huawei.

Reviewer Services

Reviewer for NeurIPS, AAAI, SIGIR conferences.