Algorithm Engineer
Kuaishou TechnologyShangdi YUAN Center（上地·元中心）, No. 16, Xierqi West Road, Haidian District
Beijing, China Email: zhoujiee@buaa.edu.cn
[Google Scholar] [Github] [知乎]
I graduated from School of Software, Beihang University with a major in Software Engineering.
My research interest includes machine learning, recommendation algorithms, game theory, and constrained optimization.
News
[Sep.2024] Joined Kuaishou Technology. New journey!
[Aug.2024] One paper got accepted by Recsys'24!
[Feb.2023] One paper got accepted by ICDE'23!
[Nov.2022] One paper got accepted by AAAI'23!
[Aug.2021] Joined Meituan Inc. New journey!
[Mar.2021] One paper DCRNN: A deep cross approach based on rnn for partial parameter sharing in multi-task learning was published!
[May.2019] Joined Xiaomi Inc. New journey!
[Jun.2017] Joined JD Inc. New journey!
[Dec.2016] One paper got accepted by The Administration and Technique of Environmental Monitoring!
Selected Publications
*: corresponding author #: equal contribution
2024
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
Wenhao Li#, Jie Zhou#, Chuan Luo, Chao Tang, Kun Zhang, and Shixiong Zhao*.
RecSys'24: Proceedings of the 18th ACM Conference on Recommender Systems, 2024. Oral
[ Paper (preprint) ]
[ DOI ]
[ Code ]
[ Bibtex ]
@inproceedings{li2024scene,
title={Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction},
author={Li, Wenhao and Zhou, Jie and Luo, Chuan and Tang, Chao and Zhang, Kun and Zhao, Shixiong},
booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
pages={370--379},
year={2024}
}
2023
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
Jie Zhou#, Xianshuai Cao, Wenhao Li, Chuan Luo, and Qian Yu*.
IEEE 39th International Conference on Data Engineering (ICDE), 2023. Oral
[ Paper (preprint) ]
[ Paper ]
[ Code ]
[ Bibtex ]
@inproceedings{zhou2023hinet,
title={Hinet: Novel multi-scenario \& multi-task learning with hierarchical information extraction},
author={Zhou, Jie and Cao, Xianshuai and Li, Wenhao and Bo, Lin and Zhang, Kun and Luo, Chuan and Yu, Qian},
booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)},
pages={2969--2975},
year={2023},
organization={IEEE}
}
Feature Decomposition for Reducing Negative Transfer: a Novel Multi-task Learning Model for Recommender System
Jie Zhou, Qian Yu*, Chuan Luo, Jing Zhang.
Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
[ Paper (preprint) ]
[ Paper ]
[ Code ]
[ Bibtex ]
@inproceedings{zhou2023feature,
title={Feature decomposition for reducing negative transfer: a novel multi-task learning method for recommender system (student abstract)},
author={Zhou, Jie and Yu, Qian and Luo, Chuan and Zhang, Jing},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={13},
pages={16390--16391},
year={2023}
}
2021 and before
DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning
Jie Zhou#, Qian Yu*.
Analysis on the Periodic Characteristic of PM2.5 Mass Concentration in Beijing by the Wavelet Square Deviation
Jie Zhou#, Peng Yang*, Agudamu Liu, Junxiu Liu.
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
Wenhao Li#, Jie Zhou#, Chuan Luo, Chao Tang, Kun Zhang, and Shixiong Zhao*.
RecSys'24: Proceedings of the 18th ACM Conference on Recommender Systems, 2024. Oral
[ Paper (preprint) ]
[ DOI ]
[ Code ]
[ Bibtex ]
@inproceedings{li2024scene,
title={Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction},
author={Li, Wenhao and Zhou, Jie and Luo, Chuan and Tang, Chao and Zhang, Kun and Zhao, Shixiong},
booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
pages={370--379},
year={2024}
}
title={Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction},
author={Li, Wenhao and Zhou, Jie and Luo, Chuan and Tang, Chao and Zhang, Kun and Zhao, Shixiong},
booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
pages={370--379},
year={2024}
}
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
Jie Zhou#, Xianshuai Cao, Wenhao Li, Chuan Luo, and Qian Yu*.
IEEE 39th International Conference on Data Engineering (ICDE), 2023. Oral
[ Paper (preprint) ]
[ Paper ]
[ Code ]
[ Bibtex ]
@inproceedings{zhou2023hinet,
title={Hinet: Novel multi-scenario \& multi-task learning with hierarchical information extraction},
author={Zhou, Jie and Cao, Xianshuai and Li, Wenhao and Bo, Lin and Zhang, Kun and Luo, Chuan and Yu, Qian},
booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)},
pages={2969--2975},
year={2023},
organization={IEEE}
}
title={Hinet: Novel multi-scenario \& multi-task learning with hierarchical information extraction},
author={Zhou, Jie and Cao, Xianshuai and Li, Wenhao and Bo, Lin and Zhang, Kun and Luo, Chuan and Yu, Qian},
booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)},
pages={2969--2975},
year={2023},
organization={IEEE}
}
Feature Decomposition for Reducing Negative Transfer: a Novel Multi-task Learning Model for Recommender System
Jie Zhou, Qian Yu*, Chuan Luo, Jing Zhang.
Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
[ Paper (preprint) ]
[ Paper ]
[ Code ]
[ Bibtex ]
@inproceedings{zhou2023feature,
title={Feature decomposition for reducing negative transfer: a novel multi-task learning method for recommender system (student abstract)},
author={Zhou, Jie and Yu, Qian and Luo, Chuan and Zhang, Jing},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={13},
pages={16390--16391},
year={2023}
}
title={Feature decomposition for reducing negative transfer: a novel multi-task learning method for recommender system (student abstract)},
author={Zhou, Jie and Yu, Qian and Luo, Chuan and Zhang, Jing},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37},
number={13},
pages={16390--16391},
year={2023}
}
DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning
Jie Zhou#, Qian Yu*.
Analysis on the Periodic Characteristic of PM2.5 Mass Concentration in Beijing by the Wavelet Square Deviation
Jie Zhou#, Peng Yang*, Agudamu Liu, Junxiu Liu.