个人简介

Huifeng currently works as a senior researcher at the Noah’s Ark Lab, Huawei Technologies. His main research interests include Recommendation, Information Retrieval and Advertising, especially developing advanced Deep Learning based models, algorithms and training technologies. The most recent focus is on how to utilize LLM to improve the recommender systems and design hardware-friendly models. He has published tens of papers at the Top-tier Conferences and Journals, such as KDD, SIGIR, IJCAI, AAAI, CIKM, WSDM, TOIS, ACM Survey. A significant proportion of the proposed works have been deployed into the products of Huawei or other companys. 

工作经历

Senior Researcher & Expert, (Noah’s Ark Lab, Huawei) Shenzhen, Guangdong 10/2018 - present
Intern, (Noah’s Ark Lab, Huawei) Shenzhen, Guangdong 04/2016 - 04/2018 (Mentor: Ruiming Tang & Zhenguo Li)

教育经历

Ph.D, Computer Science, Harbin Institute of Technology Shenzhen, CHINA 2014-2018 (Supervised by Prof. Yunming Ye)
Master, Computer Science, Harbin Institute of Technology, Shenzhen, CHINA 2012-2014  (Supervised by Prof. Yunming Ye)
BSc, Computer Science, Lanzhou University, Lanzhou, CHINA 2008-2012

论文发表

Selected

• Deepfm: A factorization-machine based neural network for CTR prediction , Huifeng Guo∗, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, IJCAI 2017.  CCF-A, deployed or integrated as a benchmark model into Huawei , Baidu Paddle, Alibaba PAI, AWS Service, Pytorch, NVDIA Merlin.

• An embedding learning framework for numerical features in ctr prediction, Huifeng Guo∗, Bo Chen∗, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He, KDD 2021. CCF-A
• Scalefreectr: Mixcache-based distributed training system for ctr models with huge embedding table, Huifeng Guo∗, Wei Guo∗, Yong Gao, Ruiming TangB, Xiuqiang He, Wenzhi Liu, SIGIR 2021. CCF-A
• Holistic Neural Network for CTR Prediction, Huifeng Guo∗, Ruiming Tang, Yunming Ye, Xiuqiang He, WWW 2017.  CCF-A
• Numerical Feature Representation with Hybrid N-ary Encoding, Bo Chen∗, Huifeng Guo∗, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He, CIKM 2022. CCF-B
• End-to-end deep reinforcement learning based recommendation with supervised embedding, Feng Liu∗, Huifeng Guo∗, Xutao Li∗, Ruiming Tang, Yunming Ye, Xiuqiang He, WSDM 2020. CCF-B
• PAL: a position-bias aware learning framework for CTR prediction in live recommender systems , Huifeng Guo∗, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang, RecSys 2019.  CCF-B
• A graph-based push service platform, Huifeng Guo∗, Ruiming Tang, Yunming Ye, et.al., DASFAA 2017. CCF-B
• A novel KNN approach for session-based recommendation, Huifeng Guo∗, Ruiming Tang, Yunming Ye, et.al., PAKDD 2019. CCF-C
• Embedding Compression in Recommender Systems: A Survey , Shiwei Li∗, Huifeng Guo∗, Xing Tang, Ruiming Tang, Lu Hou, Ruixuan Li, Rui Zhang, ACM Computing Surveys, 2023. Impact factor at 2023: 16.6 
 • Single-shot Feature Selection for Multi-task Recommendations , Yejing Wang, Zhaocheng Du, Xiangyu Zhao#, Bo Chen, Huifeng Guo#, Ruiming Tang, Zhenhua Dong, SIGIR 2023. CCF-A
• AutoTransfer: Instance Transfer for Cross-Domain Recommendations , Jingtong Gao, Xiangyu Zhao#, Bo Chen, Fan Yan, Huifeng Guo#, Ruiming Tang, SIGIR 2023. CCF-A
• CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation , Yichao Wang, Huifeng Guo#, et.al., KDD 2022. CCF-A
• Unsupervised Learning Style Classification for Learning Path Generation in Online Education Platforms , Zhicheng He, Wei Xia, Kai Dong, Huifeng Guo#, Ruiming Tang#, et.al., KDD 2022. CCF-A
• Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation, Yuhao Wang, Ziru Liu, Yichao Wang, Xiangyu Zhao#, Bo Chen, Huifeng Guo#, Ruiming Tang, WSDM 2024. CCF-B
• HAMUR: Hyper Adapter for Multi-Domain Recommendation, Xiaopeng Li, Fan Yan, Xiangyu Zhao#, Yichao Wang, Bo Chen, Huifeng Guo#, Ruiming Tang, CIKM 2023. 
• IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System , Xiangyang Li, Bo Chen, Huifeng Guo#, et.al., CIKM 2022. 
• Adaptive low-precision training for embeddings in click-through rate prediction , Shiwei Li, Huifeng Guo, Lu Hou, Wei Zhang, Xing Tang, Ruiming Tang, Rui Zhang, AAAI 2023. CCF-A

2024

Shiwei Li∗, Huifeng Guo∗, Xing Tang, Ruiming Tang, Lu Hou, Ruixuan Li, Rui Zhang, Embedding Compression in Recommender Systems: A Survey: ACM Computing Surveys, 2023. Impact factor at 2023: 16.6

Yuhao Wang, Ziru Liu, Yichao Wang, Xiangyu Zhao#, Bo Chen, Huifeng Guo#, Ruiming Tang, Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation:  WSDM 2024. CCF-B

Zirui Zhu, Yong Liu, Zangwei Zheng, Huifeng Guo, Yang You， Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization: WWW 2024.

Ziru Liu, Kecheng Chen, Fengyi Song, Bo Chen, Xiangyu Zhao, Huifeng Guo, Ruiming Tang: AutoAssign+: Automatic Shared Embedding Assignment in streaming recommendation. Knowl. Inf. Syst. 66(1): 89-113 (2024)

2023

 Yejing Wang, Zhaocheng Du, Xiangyu Zhao#, Bo Chen, Huifeng Guo#, Ruiming Tang, Zhenhua Dong,  Single-shot Feature Selection for Multi-task Recommendations: SIGIR 2023. CCF-A
 Jingtong Gao, Xiangyu Zhao#, Bo Chen, Fan Yan, Huifeng Guo#, Ruiming Tang, AutoTransfer: Instance Transfer for Cross-Domain Recommendations: SIGIR 2023. CCF-A

Xiaopeng Li, Fan Yan, Xiangyu Zhao#, Yichao Wang, Bo Chen, Huifeng Guo#, Ruiming Tang, HAMUR: Hyper Adapter for Multi-Domain Recommendation: CIKM 2023. 

Shiwei Li, Huifeng Guo, Lu Hou, Wei Zhang, Xing Tang, Ruiming Tang, Rui Zhang, Adaptive low-precision training for embeddings in click-through rate prediction. AAAI 2023. CCF-A

Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian: Diffusion Augmentation for Sequential Recommendation. CIKM 2023.

Wei Guo , Chenxu Zhu , Fan Yan , Bo Chen , Weiwen Liu , Huifeng Guo , Hongkun Zheng , Yong Liu , Ruiming Tang: DFFM: Domain Facilitated Feature Modeling for CTR Prediction. CIKM 2023

Chang Meng , Hengyu Zhang , Wei Guo , Huifeng Guo , Haotian Liu , Yingxue Zhang , Hongkun Zheng , Ruiming Tang , Xiu Li , Rui Zhang : Hierarchical Projection Enhanced Multi-behavior Recommendation. KDD 2023.

Yuhao Wang , Xiangyu Zhao, Bo Chen , Qidong Liu , Huifeng Guo , Huanshuo Liu, Yichao Wang, Rui Zhang, Ruiming Tang: PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations. SIGIR 2023.

Chenxu Zhu , Bo Chen , Huifeng Guo , Hang Xu , Xiangyang Li , Xiangyu Zhao , Weinan Zhang , Yong Yu , Ruiming Tang : AutoGen: An Automated Dynamic Model Generation Framework for Recommender System. WSDM 2023. 

Ruiming Tang, Bo Chen , Yejing Wang , Huifeng Guo, Yong Liu, Wenqi Fan , Xiangyu Zhao: AutoML for Deep Recommender Systems: Fundamentals and Advances. WSDM 2023.

Wei Guo , Chang Meng , Enming Yuan , Zhicheng He , Huifeng Guo , Yingxue Zhang , Bo Chen , Yaochen Hu , Ruiming Tang , Xiu Li , Rui Zhang : Compressed Interaction Graph based Framework for Multi-behavior Recommendation. WWW 2023.

Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang: Multi-Task Deep Recommender Systems: A Survey.  ARXIV.

Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang: How Can Recommender Systems Benefit from Large Language Models: A Survey. ARXIV.

Hengyu Zhang, Chang Meng, Wei Guo, Huifeng Guo, Jieming Zhu, Guangpeng Zhao, Ruiming Tang, Xiu Li: Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction. ARXIV.

Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang: Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation. ARXIV.

Zichuan Fu, Xiangyang Li, Chuhan Wu, Yichao Wang, Kuicai Dong, Xiangyu Zhao, Mengchen Zhao, Huifeng Guo, Ruiming Tang: A Unified Framework for Multi-Domain CTR Prediction via Large Language Models. ARXIV.

2022

Bo Chen∗, Huifeng Guo∗, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He, Numerical Feature Representation with Hybrid N-ary Encoding:  CIKM 2022. CCF-B

Yichao Wang, Huifeng Guo#, et.al., CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation: KDD 2022. CCF-A
Zhicheng He, Wei Xia, Kai Dong, Huifeng Guo#, Ruiming Tang#, et.al., Unsupervised Learning Style Classification for Learning Path Generation in Online Education Platforms: KDD 2022. CCF-A

 Xiangyang Li, Bo Chen, Huifeng Guo#, et.al.,  IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System: CIKM 2022. 

Niannan Xue , Bin Liu , Huifeng Guo , Ruiming Tang, Fengwei Zhou , Stefanos Zafeiriou , Yuzhou Zhang, Jun Wang, Zhenguo Li: AutoHash: Learning Higher-Order Feature Interactions for Deep CTR Prediction. IEEE Trans. Knowl. Data Eng. 34(6): 2653-2666 (2022)

Fuyuan Lyu , Xing Tang , Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Xue Liu: OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction. CIKM 2022.

Hengyu Zhang , Enming Yuan, Wei Guo, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen , Xiu Li, Ruiming Tang: Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks. CIKM 2022.

Wei Guo, Can Zhang, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen , Ruiming Tang, Xiuqiang He, Rui Zhang: MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction. ICDE 2022.

Fuyuan Lyu, Xing Tang , Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, Xue Liu: Memorize, Factorize, or be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction. ICDE 2022.

Fengyi Song, Bo Chen , Xiangyu Zhao, Huifeng Guo, Ruiming Tang: AutoAssign: Automatic Shared Embedding Assignment in Streaming Recommendation. ICDM 2022.

Yankai Chen, Yifei Zhang, Huifeng Guo, Ruiming Tang, Irwin King: An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching. AACL/IJCNLP (2) 2022.

Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma , Ruiming Tang, Jingjie Li, Irwin King : Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation. KDD 2022.

Enming Yuan, Wei Guo, Zhicheng He, Huifeng Guo, Chengkai Liu, Ruiming Tang: Multi-Behavior Sequential Transformer Recommender. SIGIR 2022.

Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang: Automated Machine Learning for Deep Recommender Systems: A Survey. ARXIV.

Before 2022

Huifeng Guo∗, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Deepfm: A factorization-machine based neural network for CTR prediction： IJCAI 2017.  CCF-A, deployed or integrated as a benchmark model into Huawei , Baidu Paddle, Alibaba PAI, AWS Service, Pytorch, NVDIA Merlin.

Huifeng Guo∗, Ruiming Tang, Yunming Ye, Xiuqiang He, Holistic Neural Network for CTR Prediction: WWW 2017.  CCF-A

Feng Liu∗, Huifeng Guo∗, Xutao Li∗, Ruiming Tang, Yunming Ye, Xiuqiang He, End-to-end deep reinforcement learning based recommendation with supervised embedding:  WSDM 2020. CCF-B
Huifeng Guo∗, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang, PAL: a position-bias aware learning framework for CTR prediction in live recommender systems:  RecSys 2019.  CCF-B
Huifeng Guo∗, Ruiming Tang, Yunming Ye, et.al.,  A graph-based push service platform: DASFAA 2017. CCF-B
Huifeng Guo∗, Ruiming Tang, Yunming Ye, et.al., A novel KNN approach for session-based recommendation: PAKDD 2019. CCF-C

Huifeng Guo∗, Bo Chen∗, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He, An embedding learning framework for numerical features in ctr prediction: KDD 2021. CCF-A
Huifeng Guo∗, Wei Guo∗, Yong Gao, Ruiming TangB, Xiuqiang He, Wenzhi Liu, Scalefreectr: Mixcache-based distributed training system for ctr models with huge embedding table, SIGIR 2021. CCF-A

Qiming Zheng, Quan Chen, Kaihao Bai, Huifeng Guo, Yong Gao, Xiuqiang He, Minyi Guo: BiPS: Hotness-aware Bi-tier Parameter Synchronization for Recommendation Models. IPDPS 2021.

Wei Guo, Rong Su, Renhao Tan , Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, Xiuqiang He: Dual Graph enhanced Embedding Neural Network for CTR Prediction. KDD 2021.

Yong Gao, Huifeng Guo, Dandan Lin, Yingxue Zhang, Ruiming Tang, Xiuqiang He: Content Filtering Enriched GNN Framework for News Recommendation. ARXIV.

Feng Liu, Ruiming Tang, Huifeng Guo, Xutao Li, Yunming Ye, Xiuqiang He: Top-aware reinforcement learning based recommendation. Neurocomputing 417: 255-269 (2020).

Feng Liu, Ruiming Tang, Xutao Li, Weinan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang, Xiuqiang He: State representation modeling for deep reinforcement learning based recommendation. Knowl. Based Syst. 205: 106170 (2020).

Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates: GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems. CIKM 2020.

Jianing Sun, Wei Guo , Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates: A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. KDD 2020.

Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, Zhenguo Li: AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction. SIGIR 2020.

Jianing Sun, Yingxue Zhang, Wei Guo , Huifeng Guo, Ruiming Tang, Xiuqiang He, Chen Ma , Mark Coates: Neighbor Interaction Aware Graph Convolution Networks for Recommendation. SIGIR 2020.

Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He: Multi-Branch Convolutional Network for Context-Aware Recommendation. SIGIR 2020.

Feng Liu, Wei Guo, Huifeng Guo, Ruiming Tang, Yunming Ye, Xiuqiang He: Dual-attentional Factorization-Machines based Neural Network for User Response Prediction. WWW 2020.

Yichao Wang , Huifeng Guo, Ruiming Tang, Zhirong Liu, Xiuqiang He: A Practical Incremental Method to Train Deep CTR Models. ARXIV.

Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He: Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data. ACM Trans. Inf. Syst. 37(1): 5:1-5:35 (2019).

Jianing Sun, Yingxue Zhang, Chen Ma , Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He: Multi-graph Convolution Collaborative Filtering. ICDM 2019.

Wei Guo, Ruiming Tang, Huifeng Guo, Jianhua Han, Wen Yang, Yuzhou Zhang: Order-aware Embedding Neural Network for CTR Prediction. SIGIR 2019.

Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, Yuzhou Zhang: Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. WWW 2019.

Feng Liu, Ruiming Tang, Xutao Li, Yunming Ye, Huifeng Guo, Xiuqiang He: Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification. DASFAA (2) 2018.

Weiwen Liu, Ruiming Tang, Jiajin Li, Jinkai Yu, Huifeng Guo, Xiuqiang He, Shengyu Zhang: Field-aware probabilistic embedding neural network for CTR prediction. RecSys 2018.

Huifeng Guo*, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Zhenhua Dong: DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction. ARXIV.

Huifeng Guo*, Dian-Hui Chu, Yunming Ye, Xutao Li, Xixian Fan: BLM-Rank: A Bayesian Linear Method for Learning to Rank and Its GPU Implementation. IEICE Trans. Inf. Syst. 99-D(4): 896-905 (2016).

Xiaohui Huang, Yunming Ye, Huifeng Guo, Yi Cai, Haijun Zhang, Yan Li: DSKmeans: A new kmeans-type approach to discriminative subspace clustering. Knowl. Based Syst. 70: 293-300 (2014).