I received a Ph.D. degree in Computer Science from the University of Electronic Science and Technology, Chengdu, China in 2015. I was a visiting scholar from October 2012 to October 2014 at the University of Tennessee, USA. Currently, I work at the Chongqing Institute of Green and Intelligent Technology (CIGIT), Chinese Academy of Sciences (CAS) as an Associate Professor. I was selected into the "Youth Innovation Promotion Association" of the Chinese Academy of Sciences and the "Western Light of the West Young Scholars" program of the Chinese Academy of Sciences.

Reinforcement Learning

[1]. The First Prize for Chongqing Science and Technology Progress Award in 2020;

[2]. Best Paper Nomination Award of CCF BigData’2020;

[3]. Best Paper Nomination Award of ICNSC’16;

[1].Xiaoyu Shi, Quanliang Liu, Hong Xie, Di Wu, Bo Peng, Mingsheng Shang Defu Lian, Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning Approach, Vol. 42, No. 2, ACM Transactions on Information Systems (TOIS), (CCF-A类期刊, 中科院二区, IF 5.6);

[2]. Xiaoyu Shi, Quanling Liu and Mingsheng Shang. Towards Long-term Fairness in Interactive Recommendation: A Maximum Entropy Reinforcement Learning Approach. IEEE International Conference on Web Services (ICWS 2023). (CCF-B类会议);

[3].Bingchao Wang, Xiaoyu Shi*, and Mingsheng Shang. A Self-decoupled Interpretable Prediction Framework for Highly-Variable Cloud Workloads. in International Conference on Database Systems for Advanced Applications (DASFAA), 2023, pp. 588–603. (CCF-B类会议);

[4] Zihui Zhao, Xiaoyu Shi*, and Mingsheng Shang. Performance and cost-aware task scheduling via deep reinforcement learning in cloud environment. in International Conference on Service-Oriented Computing, 2022, pp. 600–615. (CCF- B类会议);

[5] Xiaoyu Shi, Qiang He, Xin Luo, Yanan Bai, and Mingsheng Shang. Large-scale and scalable latent factor analysis via distributed alternative stochastic gradient descent for recommender systems. IEEE Transactions on Big Data vol. 8, no. 2, p. 420–431, 2022. (CCF-C,中科院二区, IF: 7.031)

[6]Qingxian Wang, Suqiang Wu, Yanan Bai, Quanliang Liu, and Xiaoyu Shi*. Neighbor Importance-aware Graph Collaborative Filtering for Item Recommendation. Neurocomputing, p. 126429, 2023, (CCF-C,中科院二区, IF:6) ;

[7] Xiaoyu Shi, Quanliang Liu, Yanan Bai, and Mingsheng Shang. RTiSR: a review-driven time interval-aware sequential recommendation method. Journal of Big Data, vol. 10, no. 1, p. 32, 2023. (中科院二区, IF:7.914);

[8] Tianqi Shang, Xinxin Li, Xiaoyu Shi*, and Qingxian Wang. Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD), 2021, pp. 524–536. (CCF推荐C类会议)

[9]. Hong Xie, Mingze Zhong, Xiaoyu Shi, Xiaoying Zhang, Jiang Zhong, Mingsheng Shang. Probabilistic Modeling of Assimilate-Contrast Effects in Online Rating Systems. IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2023.3292352. (CCF-A类期刊, 中科院二区, IF 8.412);

[10] Xiaoyu Xu, Xiaoyu Shi, and Mingsheng Shang. Graph neural networks via contrast between separation and aggregation for self and neighborhood. Expert Systems with Applications, vol. 224, p. 119994, 2023. (CCF-C类期刊, 中科院一区, IF 8.412).