Zhou, Xiuze (周秀泽)

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Senior Research Scientist,
AI Research Institute, Hithink RoyalFlush
Hangzhou, China
Research Fellow,
School of Smart Education, Jiangsu Normal University
Xuzhou, China
E-mail: zhouxiuze@foxmail.com

About me

I received the B.S. degree from the Zhejiang University of Science and Technology, in 2012, and the M.S. degree from Xiamen University, in 2016.
I am currently a senior research scientist with the AI Research Institute, Hithink RoyalFlush, China, and a research fellow with the School of Smart Education, Jiangsu Normal University, China.

As a dedicated professional in the field of AI, I am committed to leveraging advanced technologies to address real-world challenges. My research has led to significant contributions in various publications, showcasing my commitment to advancing AI across practical applications such as recommendation systems, healthcare, and Battery Management Systems (BMS).


Research interests

  • Recommendation Systems

  • Large Language Model

Current work

  • RecLLM: Large Language Model for Explainable Recommendation

  • Large Language Model for Generation of Medical Diagnostic Reports

  • Battery-GPT: A Large Language Model Predicting the State of Health and Remaining Useful Life of Lithium-ion Batteries

  • Semi-supervised for Recommendation

Under review

  1. K. Li, X. Zhou*, "Layer-wise Contrastive Learning BERT for Sentence Representation".

  2. X. Zhou*, "Graph Neural Network with Transformer for Sparsity Problem of Recommender Systems".

  3. J. Cheng, D. Chen, and X. Zhou*, "Graph-based Feature Crossing to Enhance Recommender Systems".

  4. C. Jin, and X. Zhou*, "GraphAE: Graph AutoEncoders for Drug-Target Interaction Prediction".

Recent publications

  1. D. Chen, X. Zhou*, "AttMoE: Attention with Mixture of Experts for Remaining Useful Life Prediction of Lithium-Ion Batteries", Journal of Energy Storage, Apr. 2024. (IF = 9.4) [pdf][code]

  2. Y. Lin, W. Zhang, F. Lin*, W. Zeng, X. Zhou*, and P. Wu, "Knowledge-aware Reasoning with Self-supervised Reinforcement Learning for Explainable Recommendation in MOOCs", Neural Computing and Applications, Dec. 2023. (IF = 6.0)

  3. Y. Ding, S. Jia, T. Ma*, B. Mao, X. Zhou, L. Liu, and D. Han, "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction", Workshop of IJCAI2023, 2023. [arxiv.org].

  4. W. Zhang, Y. Lin, Y. Liu, P. Wu, F. Lin*, and X. Zhou*, "Self-Supervised Reinforcement Learning with Dual-reward for Knowledge-aware Recommendation", Applied Soft Computing, Oct. 2022. (IF = 8.263) [pdf][code]

  5. M. Chen, T. Ma, and X. Zhou*, "CoCNN: Co-occurrence CNN for Recommendation", Expert Systems with Applications, Jun. 2022, 195, pp. 116595. (IF = 8.665) [code]

  6. M. Chen, Y. Li, X. Zhou*, "CoNet: Co-occurrence Neural Networks for Recommendation", Future Generation Computer Systems, Nov. 2021, 124, pp. 308-314. (IF = 7.307) [code]

  7. M. Chen, X. Zhou*, "DeepRank: Learning to Rank with Neural Networks for Recommendation", Knowledge-Based Systems, Dec. 2020, 209, pp. 106478. (IF = 8.139) [code]

  8. D. Chen, W. Hong, and X. Zhou*, "Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries", IEEE Access, 2022, 10, pp. 19621-19628. (IF = 3.367) [pdf][code]

  9. X. Wu, W. Zeng, F. Lin*, and X. Zhou, "NeuRank: Learning to Ranking with Neural Networks for Drug-Target Interaction Prediction", BMC Bioinformatics, Nov. 2021, 22, pp. 567. (IF = 3.328) [pdf][code]

  10. X. Zhou* and S. Wu, "Rating LDA Model for Collaborative Filtering", Knowledge-Based Systems, Oct. 2016, 110, pp. 135-143. (IF = 8.139) [pdf]

  11. K. Li, X. Zhou, F. Lin*, W. Zeng, B. Wang, and G. Alterovitz, "Sparse Online Collaborative Filtering with Dynamic Regularization", Information Sciences, Dec. 2019, 505, pp. 535-548. (IF = 8.233) [pdf]

  12. X. Zhou, W. Shu, F. Lin*, and B. Wang, "Confidence-Weighted Bias Model for Online Collaborative Filtering", Applied Soft Computing, Sep. 2018, 70, pp. 1042-1053. (IF = 8.263)

  13. K. Li, X. Zhou, F. Lin*, W. Zeng, and G. Alterovitz, "Deep Probabilistic Matrix Factorization Framework for Online Collaborative Filtering", IEEE Access, Mar. 2019, 7, pp. 56117-56128. (IF = 3.367)

  14. F. Lin, X. Zhou, and W. Zeng*, "Sparse Online Learning for Collaborative Filtering", International Journal of Computers Communications & Control, Apr. 2016, 11 (2), pp. 248-258. (IF = 2.093)

  15. S. Lu, H. Chen, X. Zhou, B. Wang, H. Wang*, and Q. Hong, "Graph-Based Collaborative Filtering with MLP", Mathematical Problems in Engineering, Dec. 2018, 2018, pp. 1-10. (IF = 1.305)

  16. X. Zhou, F. Lin*, L. Yang, J. Nie, Q. Tan, W. Zeng, and N. Zhang, "Load Balancing Prediction Method of Cloud Storage based on Analytic Hierarchy Process and Hybrid Hierarchical Genetic Algorithm", SpringerPlus, Nov. 2016, 5 (1), pp. 1989-2012. (IF = 1.780)

  17. X. Zhou*, and S. Wu, "The Biterm Author Topic in the Sentences Model for E-Mail Analysis", IEICE Transactions on Information and Systems, Aug. 2017, E100.D (8), pp. 1852-1859. (IF = 0.449)

Note: * indicates the corresponding author.

Full list of publications in Google Scholar.

Academic service


  • IEEE Transactions on Neural Networks and Learning Systems

  • IEEE Transactions on Industrial Informatics

  • ACM Transactions on Knowledge Discovery from Data

  • IEEE Access

More details in Publons


  1. Large Language Models-based Code Generation and Review, 03.2023-Present

    • Collect and process a large corpus of code snippets, programming tutorials, and relevant documentation

    • Pre-train LLMs and accelerate training and inference of the large model on multi-machine multi-GPUs

    • Iterate on the code generation and review process

  2. Stable Diffusion-based AI Painting, 01.2022-12.2022

    • Explore and design prompt

    • Accelerate training and inference for diffusion process

    • Deploy trained models in the appropriate environment

  3. Advertising Platform Development, 01.2021-12.2021

    • Provide advertising strategies and solutions for advertisers to maximize revenue

    • Provide automated advertising instead of manual selection

    • Use users' history information to build their profiles, and then select the target users

  4. Campus Recommender System, 03.2021-12.2021

    • Built user profiles based on the data crawled from websites

    • Recommended information, such as courses from MOOC, and publications from Arxiv, to students

    • Recommended information from within and outside the university based on faculty research, courses taught, and interests

  5. Online Education Explainable Recommender System, NSFC, 06.2018-12.2018

    • Summarized over 500,000 exercises and classified their knowledge points from all subjects

    • Applied matrix factorization for online learning and recommendation of exercises based on interaction of users

    • Added latent features learned by neural networks from exercises to online matrix factorization for better performance


M.E., Pattern Recognition and Intelligent Systems, Xiamen University, 06.2016

  • Awards: Principal Level Scholarship (1st in admission)

  • Main Courses: Machine Learning, Design of Neural Networks, Digital Image Processing, Time Series Analysis, Pattern Recognition, Data Mining and Its Application, Artificial Intelligent: Theory and Application, Recommender System.

B.E., Automation, Zhejiang University of Science and Technology, 06.2012

  • Main Courses: C Programming, Embedded Systems, Computer Network and Communication, Computer Control System.

Work experience

  1. Researcher, School of Smart Education, Jiangsu Normal University, 03.2023-Present

    • Instructed graduate students in scientific research

    • Make regular presentations and exchanges

  2. Research Scientist, AI Research Institute, Hithink RoyalFlush, 06.2019-Present

    • Research the newest machine learning algorithms and recommender system technology on stocks and hot news

    • Apply neural network models to drug-target interaction prediction and evaluate the performance

    • Publish papers and apply for relevant patents for the corporation

    • Give lessons on Artificial Intelligence and Recommender Systems to the staff

  3. Research Assistant, Big Data Lab, Xiamen University, 09.2016-02.2019

    • Instructed two undergraduate and three graduate students in scientific research

    • Tracked, studied, reproduced, and improved up-to-date machine learning methods

    • Published papers on machine learning and recommender systems

  4. Software Engineer, Dragon SOFT, 07.2013-06.2014

    • Developed an electronic system of shooting game for an amusement park

    • Recorded the track of users' behavior from sensors in a database

    • Built a model analyzing users’ behavior concerning speed, acceleration and number of cylinders

  5. Assistant Engineer, Gold Electronic, 03.2012-07.2012

    • Cooperated with motor companies, such as Zotye and BYD, on battery management system development

    • Developed a testing and analytics platform for performance of a lithium battery with C# (real-time data)

    • Used CAN bus to collect working data of batteries and analyzed the data for balance power

A brief cv.