Ning Yang

Ning Yang

博士，副教授，博士研究生导师
中国计算机学会高级会员
中国计算机学会信息系统专业委员会执行委员

Ph.D, Associate Professor, Senior member of CCF

Address:
College of Computer Science
Sichuan University
Chengdu, China, 610041

Telephone: (86-028)85466105
Email: yangning at scu dot edu dot cn
Office: B421 Basic Teaching Building

[Research Interests] [Research Projects] [Brief Bio] [Publications] [Courses] [Google Scholar Profile] [Professional Services] [People]

Research Interests

Trustworthy Machine Learning (Adversarial Robustness, Fairness, Explainability, Privacy, Accountability, ... )
Recommender Systems
Learning on Graph

科研经费充足，学习和研究环境宽松，欢迎对我的研究方向感兴趣的同学申请我的硕士研究生和博士研究生。
I am looking for motivated students who are interested in my research interests.

Research Projects

Current Projects

Research on Fusion of Multiple Social Networks Based on Cross-network Information Diffusions, Supported by National Natural Science Foundation of China, General Program (Grant No. 61972270), ¥728K, 2020-2023 (国家自然科学基金面上项目, 主持)

Finished Projects

Research on Real-Time Computing and Analyzing Engine for Urban Big Data, Supported by Sichuan Province, Hightech Program (Grant No. 2019YFG0213), ¥200K, 2019-2020 (四川省支撑计划项目, 主持)
Research on Key Techniques of Large Scale Social Sensing Data Mining, Supported by the Basic Research Program of Sichuan Province with Grant No. 2014JY0220, ¥100K, 2014-2016 (四川省应用基础计划项目, 主持)
Research on the Mining of Information Transfer in Temporal Sequences, Supported by National Natural Science Foundation of China, General Program (Grant No. 61173099), ¥550K, 2012-2015 (国家自然科学基金面上项目, 主持)
Research on the Mining of Information Transfer for the Predictions and Intervenions of Temporal Sequences, Supported by the Doctoral Fund of Ministry of Education of China (Grant No. 20110181120062), ¥40K, 2012-2014 (教育部博士点基金项目, 主持)
Research on Key Techniques of the Mining and Application of Digital Traces in Internet of Things, Supported by Sichuan Province, Hightech Program (Grant No. 2011GZ0223), ¥100K, 2011-2013 (四川省支撑计划项目, 主持)

Brief Bio

I received Ph.D. in computer Science from the Sichuan University, Chengdu, China, 2010. Before coming back to campus, I was a programmer.
Visiting Scholar at University of Illinois at Chicago, Supervised by Professor Philip S. Yu (09/2012 - 08/2013)

Publications

Xinyu Zhu, Lilin Zhang, Ning Yang*. Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment. SIGIR, 2024. pdf

Zhuhang Li, Ning Yang*. Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation. DASFAA, 2024. pdf

Ziwen Du, Ning Yang*, Philip S. Yu. Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning. TKDE, 2023. Online, pdf

Qiaomin Yi, Ning Yang*, Philip S. Yu. Dual Adversarial Variational Embedding for Robust Recommendation. TKDE, 2023. Online, pdf

Yuhui Zhao, Ruichun Yang, Ning Yang*, Tao Lin, Qiuai Fu, Yuchi Ma. Robust Log-Based Anomaly Detection with Hierarchical Contrastive Learning. ICASSP, 2023. pdf

Yaxuan Ye, Ning Yang*. An Anchor-Semantics-Aware Neural Preference Propagation Model for Session-based Recommendation. IJCNN, 2023. pdf

Zhihui Zhou, Lilin Zhang, Ning Yang*. Contrastive Collaborative Filtering for Cold-Start Item Recommendation. WWW, 2023. pdf

Weijun Lin, Shijie Ding, Zheng Li, and Ning Yang*. A Three-Step Calibration Method of Sensors’ Pose for Magnetic Localization System. TIM, 2023. Online

Rui Wang, Ning Yang*, Philip S. Yu. Learning Aspect-Level Complementarity for Intent-Aware Complementary Recommendation. Knowledge-Based Systems, 2022. online, pdf

Lihua Chen, Ning Yang*, Philip S. Yu. Time Lag Aware Sequential Recommendation. CIKM, 2022. pdf

Xiaoyun Zhao, Ning Yang*, Philip S. Yu. Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning. WSDM, 2022. preprint ACM DL Code

Chen Cui, Ning Yang*, Philip S. Yu. MLANE: Meta-Learning Based Adaptive Network Embedding. IEEE BigData, 2020. pdf

Ning Yang, Yuchi Ma, Li Chen, Philip S. Yu. A Meta-feature Based Unified Framework for Both Cold-start and Warm-start Explainable Recommendations. World Wide Web Journal, 2020, 23(1):241–265. pdf

Yuhui Zhao, Ning Yang*, Tao Lin, Philip S. Yu. Deep Collaborative Embedding for Information Cascade Prediction. Knowledge-Based Systems, 2020, 193(4):105502. online, pdf

Huanrui Luo, Ning Yang*, Philip S. Yu. Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations. IEEE BigData, 2019. (Best Paper Award, out of 554 submissions) pdf

Yi Wu, Ning Yang*, Huanrui Luo. Unified Group Recommendation Towards Multiple Criteria. APWeb-WAIM, 2019.pdf

Yunfeng Hou, Ning Yang*, Yi Wu, Philip S. Yu. Explainable Recommendation with Fusion of Aspect Information. World Wide Web Journal, 2019, 22(1):221-240. pdf

Yanchao Sun, Cong Qian, Ning Yang*, Philip S. Yu. Collaborative Inference of Coexisting Information Diffusions. ICDM, 2017. pdf

Yuchi Ma, Ning Yang*, Lei Zhang, Philip S. Yu. Spatial and Semantical Label Inference for Social Media: A Cross-Network Data Fusion Approach. Knowledge and Information Systems (KAIS), 2017, 53(10):153–177.pdf

Ning Yang, Lifang He, Zheng Li, Philip S. Yu. Reducing Uncertainty of Dynamic Heterogeneous Information Networks: A Fusing Reconstructing Approach. Data Mining and Knowledge Discovery (DMKD), 2017, 31(3):879-906. pdf

Yuchi Ma, Ning Yang*, Lei Zhang, Philip S. Yu. Predicting Neighbor Label Distributions in Dynamic Heterogeneous Information Networks. World Wide Web Journal, 2017, 20(6):1269-1291. pdf

Ning Yang, Philip S. Yu. Efficient Hidden Trajectory Reconstruction From Sparse Data. CIKM, 2016. pdf

Duoduo Zhang, Yuchi Ma, Ning Yang*. Explicable Location Prediction Based on Preference Tensor Model. WAIM 2016. pdf

Yuchi Ma, Ning Yang*, Chuan Li, Lei Zhang, Philip S. Yu. Predicting Neighbor Distribution in Heterogeneous Information Networks. SDM, 2015. pdf

Ning Yang, Xiangnan Kong, Fengjiao Wang, Philip S. Yu. When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events. SDM, 2014. pdf

Courses

Principles of Database System (Fall 2020, for undergraduates)

Professional Services

CCF Senior Member
ACM Member
ACM SIGKDD Member
IEEE Computer Society Member
Reviewer: CIKM, AAAI, WAIM, TKDE ...