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, PLMs, or multimodal pretraining. 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 60+ papers in top conferences such as NeurIPS, ACL, SIGIR, CVPR, MM, WWW, etc., which have received . Please see below for some selected publications grouped by research topics. For the full list of publications, please visit my GoogleScholar.

*: indicates co-first or correponding author

Recommender System

Making the Full Model Adaptive: Multi-level Domain Adaptation for Multi-Domain CTR Prediction, Qi Zhang, Chuhan Wu, Jieming Zhu, Jingjie Li, Qinglin Jia, Ruiming Tang, Rui Zhang and Liangbi Li. In DLP@RecSys 2023.

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.

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation, Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, Xiao-Ming Wu. In WWW 2023.

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

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.

Pretraining for Recommendation

Generative Retrieval as A New Paradigm: A Survey, Yan Xia, Wang Lin, Ye Wang, Jiahao Xun, Linjun Li, Jieming Zhu*, Quanyu Dai, Zhenhua Dong and Zhou Zhao. In GenRec@CIKM 2023.

Contrastive Quantization based Semantic Code for Generative Recommendation, Mengqun Jin, Zexuan Qiu, Jieming Zhu*, Zhenhua Dong and Xiu Li. In GenRec@CIKM 2023.

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.

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.

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.

Multimodal Understanding & Generation

UniCode: 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.

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.

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.

Log Analysis

Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics, Jieming Zhu, Shilin He, Pinjia He, Jinyang Liu, Michael R. Lyu. In ISSRE 2023.

Tools and Benchmarks for Automated Log Parsing, Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu. In ICSE 2019.

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression, Jinyang Liu, Jieming Zhu, Shilin He, Pinjia He, Zibin Zheng, Michael R. Lyu. In ASE 2019.

Drain: An Online Log Parsing Approach with Fixed Depth Tree, Pinjia He, Jieming Zhu, Zibin Zheng, Michael R. Lyu. In ICWS 2017.

Experience Report: System Log Analysis for Anomaly Detection, Shilin He, Jieming Zhu, Pinjia He, Michael R. Lyu. In ISSRE 2016.

Learning to Log: Helping Developers Make Informed Logging Decisions, Jieming Zhu, Pinjia He, Qiang Fu, Hongyu Zhang, Michael R. Lyu, Dongmei Zhang. In ICSE 2015.

Honors & Awards

Professional Services


DataFun Summit 2021 Talk: 预训练模型在信息流推荐中的应用与探索.

Reviewer Services

Serving as Program Committee & Reviewer for NeurIPS, CVPR, AAAI, KDD, SIGIR conferences.