Yingqiang Ge (葛英强)

About Me


I’m Yingqiang Ge, an Applied Scientist at Amazon. I earned my Ph.D. degree in the Computer Science Department at Rutgers University, New Brunswick, NJ in 2023, under the supervision of Prof. Yongfeng Zhang. Before that, I received M.S. degree from Rutgers University in 2019 and B.S. degree from Zhengzhou University in 2017.

My research interests lie in the interface of Machine Learning and Information Retrieval with specific focus on: Recommender System, Economic Recommendation, Explainable Recommendation, Fairness-aware Recommendation and Causal Recommendation. In addition, I also explore the interplay between recommender systems and various perspectives of Trustworthy AI.

These days, I am fascinated by foundation models (e.g., GPT-4, LLaMA), which are trained on vast data using self-supervision at an immense scale and can be tailored for a wide range of downstream tasks. Foundation models present a new perspective on AI system development and human-AI interaction. In the context of personalization, it is essential to investigate how to improve our understanding of their functioning and integrate them into personalized scenarios or systems, while optimizing their efficiency, modularity, robustness and trustworthiness.


  • 2024-02-19 Our survey on Trustworthy Recommender Systems has been accepted for publication in ACM Transactions on Recommender Systems.
  • 2023-12-11 Started a new position as Applied Scientist at Amazon.
  • 2023-10-24 Received NeurIPS 2023 Scholar Award.
  • 2023-10-19 Gave a guest lecture for CS 483 (Big Data Mining), Department of Computer Science, UIC.
  • 2023-10-17 Successfully defended my PhD degree on the topic of “Towards Trustworthy Recommender Systems” and proudly hold the title of Dr.!
  • 2023-09-22 Our research framework for LLM-based Agents, OpenAGI accepted by NeurIPS’23, Datasets and Benchmarks.
  • 2023-08-04 Our research on “Logistics Audience Expansion” accepted by CIKM’23.
  • 2023-07-17 Our survey on “Fairness in Recommendation” accepted by ACM TIST’23.
  • 2023-07-15 Our research on “User-Controllable Recommendation” accepted by ECAI’23
  • 2023-05-16 Gave a presentation “When LLMs Meet Domain Experts” at NEC Laboratories America.
  • 2022-12-02 Gave a presentation “Towards Fairer Recommender Systems through Deep Reinforcement Learning” at the Institute of Computing Technology, Chinese Academy of Sciences.
  • 2022-10-18 Our research on “Counterfactual Data Augmentation” accepted by WSDM’23.
  • 2022-06-28 Our research on “Federated Fairness” and “P5” accepted by RecSyc’22.
  • 2022-06-09 Gave a talk on “Fairness in Recommender System” in Amazon.
  • 2022-05-24 Successfully passed my PhD Qualifying Exam!
  • 2022-04-26 Received SIGIR 2022 Student Travel Grant.
  • 2022-03-25 Our research on “Explainable Fairness” and “AutoLoss” are accepted by SIGIR 2022.
  • 2022-03-09 Our research on “Fairness Evaluation” has been accepted for publication in the Journal of the Association for Information Science and Technology (JASIST).
  • 2022-02-24 Our research on “Explainable Recommendation through Visualization” has been accepted to appear at the ACL 2022 Main conference.
  • 2022-01-13 Our research on “Explainable Recommendation over Knowledge Graphs”, “Explainable GNN” are accepted as long papers on the Web Conference 2022.
  • 2021-12-27 Received ACM WSDM’22 Student Travel Awards.
  • 2021-10-11 Our research on “Pareto Efficient Fairness-Utility Trade-off” has been accepted as full paper by WSDM 2022.
  • 2021-08-09 Our proposal “Fairness of Machine Learning in Recommender Systems” has been accepted for presentation as a tutorial at the CIKM 2021.
  • 2021-08-08 Our research on “Counterfactual explainable recommendation” has been accepted by CIKM 2021.
  • 2021-04-27 Our proposal “Fairness of Machine Learning in Recommender Systems” has been accepted for presentation as a tutorial at the SIGIR 2021.
  • 2021-04-14 Our research on “Personalized Fairness” is accepted by SIGIR 2021.
  • 2021-01-17 Received SIGIR Student Travel Grant.
  • 2021-01-16 Papers on “Fairness”, “Generative Recommendadtion”, “Knowledge Graph Embedding” accepted to WWW 2021.
  • 2020-10-16 Our research on “Long-term Fairness” is accepted as a full paper to the WSDM 2021 conference.
  • 2020-08-10 Our research on “Neural Symbolic Reasoning for Explainable Recommendadtion” is accepted by the CIKM 2020 conference.
  • 2020-06-20 Our research on “Risk-aware Recommendation”, “Fairness-aware Recommendation”, and “Echo Chamber Analysis” are accepted as long papers on the SIGIR 2020.
  • 2019-01-20 Our recent research on bridging machine learning and economic principles for economic analysis of web-based systems is accepted by the Web Conference 2019 (WWW 2019).